Dna methylation markers for neurodevelopmental syndromes

ABSTRACT

The present disclosure provides epigenetic signatures, comprising genomic CpG dinucleotide sequences, genes, and/or genomic regions, which are differentially methylated in individuals with CHARGE syndrome relative to non-CHARGE syndrome controls, and their use in methods and kits for detecting and/or screening for CHARGE syndrome, or the likelihood of CHARGE syndrome. The present disclosure also provides epigenetic signatures, comprising genomic CpG dinucleotide sequences, genes, and/or genomic regions, which are differentially methylated in individuals with Kabuki syndrome relative to non-Kabuki syndrome controls, and their use in methods and kits for detecting and/or screening for Kabuki syndrome, or the likelihood of Kabuki syndrome.

RELATED APPLICATION

This application claims the benefit of priority to U.S. Provisional Applications Nos. 62/067,073 filed Oct. 22, 2014 and 62/115,922 filed Feb. 13, 2015, respectively. The contents of which are incorporated herein by reference in their entirety.

FIELD

The disclosure relates to methods and kits for detecting and/or screening for CHARGE syndrome (CS), or an increased likelihood of CS, in a human subject. The disclosure further relates to methods and kits for detecting and/or screening for Kabuki syndrome (KS), or an increased likelihood of KS, in a human subject.

INTRODUCTION

Epigenetics, which refers to changes in gene expression that occur without a change in DNA sequence¹, is a vital genome-wide regulatory system, the primary function of which is to modulate gene expression. Epigenetic regulation determines where and when genes are expressed via a number of mechanisms including DNA methylation, histone modifications and

ATP-dependent chromatin remodeling. According to the Disease Annotated Chromatin Epigenetics Resource (DAnCER)² 633 human genes encode proteins that have experimentally confirmed involvement in regulating epigenetic modifications and chromatin remodeling. An additional ˜1,600 genes have been predicted, using bioinformatics tools, to be involved in epigenetic regulation².

To date, mutations and deletions or insertions in just over 30 of these genes with known functions in regulating the epigenome have been identified as being causative in syndromic and non-syndromic intellectual disability (S-ID and NS-ID)³⁻¹⁴. One of these genes is chromodomain helicase DNA-binding protein 7 (CHD7). A member of a family of chromatin remodeling proteins, CHD7 has been shown to be important in early embryonic development. CHD7 is expressed in human embryonic stem (hES) cells and that expression is increased, and required, for hESs to form multipotent migratory neural crest like cells (hNCLC)¹⁵. Neural crest cells (NCC) contribute to a number of tissues in the developing embryo¹⁶. In animal models, both mouse and Xenopus laevis, knockdown of CHD7 disrupts the migration of NCC¹⁷⁻¹⁹. Hemizygosity of CHD7 results in the aberrant development of craniofacial structures, heart and other organ abnormalities^(19,20).

CHARGE syndrome can be clinically characterized by the coloboma of the eye, heart defects, choanal atresia, retardation of growth and development, genital hypoplasia, and ear/deafness/vestibular/olfactory/other cranial nerve disorders²¹. Its incidence is 1 in 8 500 to 10 000 live births^(22,23.) CHARGE syndrome patients face a wide variety of life-threatening conditions, with high mortality rates in the first year of life, including cardiac abnormalities, feeding and/or breathing difficulties²³. The majority of CHARGE syndrome (OMIM #214800) cases (˜60% to 80%) are due to haploinsufficiency of CHD7, due to de novo nonsense, deletion, or missense mutations²⁴. More than 500 pathogenic mutations in CHD7 have been identified, many of which are unique to the patient^(25,26).

In human cell lines using chromatin immunoprecipitation (ChIP) CHD7 has been shown to bind to chromatin regions that are active as demonstrated by histone H3 lysine 4 methylation (H3K4) and DNAse1 hypersensitivity of these binding sites²⁷′²⁸. CHD7 binding sites in hES are localized to enhancers and promoters determined by overlapping features, including p300 binding, H3K4 mono-, di- and tri methylation²⁸. It has been previously determined that loss of function mutations in KDMSC (OMIM#314690), an H3K4 demethylase, causes alterations in DNA methylation demonstrating cross talk between DNA methylation and chromatin modification²⁹.

Phenotypic overlap between CHARGE syndrome and another neurodevelopmental syndrome, Kabuki syndrome, can sometimes lead to the consideration of CHARGE syndrome in individuals with Kabuki syndrome. Indeed, CHARGE syndrome and Kabuki syndrome are both undergrowth syndromes. Undergrowth refers to growth deficiency compared to the norms of the population and usually affects height and weight. Growth of the head may be normal or deficient Kabuki syndrome (OMIM #147920) is a disorder with a prevalence of 1 in 32,000 births, characterized by distinct facial characteristics (inverted lower eyelids, long palpebral fissures, large dysplastic ears, arched eyebrows, short nasal septum, cleft palate and abnormal teeth), various degrees of intellectual disability and other congenital malformations (cardiac, renal and skeletal)³⁴.

In 2010, mutations in the KMT2D (also known as MLL2) gene were identified as the cause of the majority of Kabuki syndrome cases³³. KMT2D, located on chromosome 12, belongs to the trithorax group of histone modifying proteins. It contains several domains suited for its function, including a PHD domain for histone binding, a FYRN domain found in chromatin associating proteins and a SET domain found in many methyltransferases. The Drosophila homolog of the KMT2D gene, trithorax-related (trr), has been demonstrated to trimethylate histone H3 lysine 4. This histone mark is commonly found in active or poised chromatin regions. Normal epigenetic marks, including DNA methylation (DNAm) and histone modifications, are established and maintained by genes that can be defined as “epigenes”. Mutations in epigenes result in a number of neurodevelopmental disorders, including Kabuki syndrome. Histone modifications and DNA methylation have been shown to interact through crosstalk between proteins and protein complexes which regulate chromatin structure. Specific histone marks are commonly associated with DNAm and methylation of specific CpG sites accompanying specific histone modifications. The present inventors have previously determined that loss of function mutations in KDMSC (OMIM#314690), an H3K4 demethylase, causes alterations in DNA methylation demonstrating cross talk between DNA methylation and chromatin modification³⁵.

There is a need for robust and cost-effective tests capable of identifying neurodevelopmental syndromes such as CHARGE syndrome cases and Kabuki syndrome cases, with high specificity and sensitivity. These tests may be used to identify CHARGE syndrome and Kabuki syndrome in individuals carrying variants of unknown significance.

SUMMARY

The present disclosure provides DNA methylation markers which are capable of differentiating CHARGE syndrome (CS) cases carrying a pathogenic CHD7 mutation from non-CHARGE syndrome (non-CS) controls, including distinguishing CHARGE syndrome cases from individuals carrying a benign CHD7 variant (benign variant as referred to herein means a variant in CHD7 gene that does not alter protein function). The DNA methylation markers and the methods of their use described herein may provide useful alternative or supplementary diagnostics to currently available methods of detecting and/or screening for CS, or likelihood of CS.

In an aspect, there is provided a method of detecting and/or screening for CHARGE syndrome (CS), or an increased likelihood of CS, in a human subject, comprising determining a sample DNA methylation profile from a sample of DNA from said subject, said sample profile comprising the methylation level of at least 3, optionally at least 5, at least 8, at least 10, at least 25, at least 44, at least 50, at least 75, at least 100, at least 125, at least 140, or all CpG loci from (i) Tables 2 and/or 16 and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i).

The method further comprises determining the level of similarity of said sample profile to one or more control profiles, wherein (i) a high level of similarity of the sample profile to a CS specific control profile; (ii) a low level of similarity to a non-CS control profile; and/or (iii) a higher level of similarity to a CS specific control profile than to a non-CS control profile indicates the presence of, or an increased likelihood of, CS.

In an embodiment, the CpG loci comprise (i) CpG loci from Tables 2 and/or 16 having an absolute CS delta-beta value ≧0.10, optionally ≧0.11, ≧0.12, ≧0.13, ≧0.15, ≧0.18, ≧0.20 or ≧0.22; and/or (ii) associated

CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i).

In another aspect, there is provided a method of detecting and/or screening for CHARGE syndrome (CS), or an increased likelihood of CS, in a human subject, comprising:

determining a sample methylation profile from a sample of DNA from said subject, said sample profile comprising the methylation level of CpG loci, wherein the CpG loci are the loci from Tables 2 and/or 16 having an absolute CS delta-beta value ≧0.1; and

determining the level of similarity of said sample profile to one or more control profiles, wherein (i) a high level of similarity of the sample profile to an CS specific control profile; (ii) a low level of similarity to a non-CS control profile; and/or (iii) a higher level of similarity to a CS specific control profile than to a non-CS control profile indicates the presence of, or an increased likelihood of, CS.

In an embodiment, the CpG loci comprise CpG loci from Tables 2 and/or 16 having an absolute CS delta-beta value ≧0.10, optionally ≧0.11, ≧0.12, ≧0.13, ≧0.15, ≧0.18, ≧0.20 or ≧0.22.

In another embodiment, determining the sample methylation profile comprises the steps:

-   a) providing the sample comprising genomic DNA from the subject; -   b) optionally, isolating DNA from the sample; -   c) optionally, treating DNA from the sample with sodium bisulfite     for a time and under conditions sufficient to convert non-methylated     cytosines to uracils; -   d) optionally, amplifying the DNA; and -   e) determining the methylation level at the CpG loci by means of     bisulfite sequencing, pyrosequencing, methylation-sensitive     single-strand conformation analysis (MS-SSCA), high resolution     melting analysis (HRM), combined bisulfite restriction analysis     (COBRA), methylation-sensitive single nucleotide primer extension     (MS-SnuPE), base-specific cleavage/MALDI-TOF, methylation-specific     PCR (MSP), methylation-sensitive restriction enzyme-based methods,     microarray-based methods, whole-genome bisulfite sequencing (WGBS,     MethylC-seq or BS-seq), reduced-representation bisulfite sequencing     (RRBS), and/or enrichment-based methods such as MeDIP-seq, MBD-seq,     or MRE-seq.

In another embodiment, the correlation coefficient is a linear correlation coefficient, optionally a Pearson correlation coefficient or a Spearman correlation coefficient.

In another embodiment, a higher level of similarity to the CS specific control profile than to the non-CS control profile is indicated by a higher correlation value computed between the sample profile and the CS specific control profile than an equivalent correlation value computed between the sample profile and the non-CS control profile, optionally wherein the correlation value is a correlation coefficient.

In yet another embodiment, a high level of similarity to the control profile is indicated by a Pearson correlation coefficient between the sample profile and the control profile having an absolute value between 0.5 to 1, optionally between 0.75 to 1, and a low level of similarity to the control profile is indicated by a correlation coefficient between the sample profile and the control profile having an absolute value between 0 to 0.5, optionally between 0 to 0.25.

In an embodiment, the methylation level is measured as a β-value.

In another embodiment, a Charge Syndrome Score (Charge score) is calculated according to following formula:

Charge score(B)=r (B, Charge profile)−r (B, non-Charge profile)

where r is a Pearson correlation coefficient, and B is a vector of DNA methylation levels across the selected methylation loci in the sample.

In another embodiment, determining the sample methylation profile comprises contacting the DNA with at least one agent that provides for determination of a CpG methylation status of at least one, optionally all, of the selected CpG loci, wherein the agent comprises an oligonucleotide-immobilized substrate comprising a plurality of capture probes, each capture probe comprising a pair of capture oligonucleotides, wherein the capture oligonucleotide pairs comprise (a) an oligonucleotide comprising nucleotide sequence complementary to or identical to a nucleotide sequence of genomic DNA comprising a selected CpG, and (b) an oligonucleotide comprising nucleotide sequence complementary to or identical to a nucleotide sequence of genomic DNA comprising the same selected CpG locus of (a), in which the cytosine residue of the CpG locus is replaced with a thymine residue.

In yet another embodiment, the contacting is under hybridizing conditions.

In an embodiment, the methylation levels of the selected loci of at least one control profile is derived from one or more samples, optionally from historical methylation data for a patient or pool of patients.

In another embodiment, the non-CS control profile comprises methylation levels for the selected CpG loci listed in Tables 2 and/or 16. In yet another embodiment, the CS specific control profile comprises DNA methylation levels for the selected CpG loci listed in Tables 2 and/or 16. In an embodiment, the methylation levels of associated CpG loci not listed in Tables 2 and/or 16 is assumed to be equivalent to the methylation level of a CpG loci listed in Tables 2 and/or 16 with which the CpG loci is associated.

In an embodiment, the sample is derived from blood, fibroblast tissue, buccal tissue, lymphoblastoid cell line, saliva or a prenatal sample. The prenatal sample is optionally a CVS, placenta, circulating fetal DNA and/or amniotic fluid sample. In another embodiment, the sample is derived from a tissue biopsy.

In another embodiment, the human subject is a fetus.

Another aspect provides a method of detecting and/or screening for CHARGE syndrome (CS), or an increased likelihood of CS, in a human subject, comprising determining a sample DNA methylation profile from a sample of DNA from said subject, said sample profile comprising the methylation level of at least 2, optionally at least 3, at least 4, at least 6, at least 8, at least 10, at least 16, at least 20, at least 25, at least 30, at least 35, at least 40, or all the genes from Tables 2 and/or 16.

The method further comprises determining the level of similarity of said sample profile to one or more control profiles, wherein (i) a high level of similarity of the sample profile to a CS specific control profile; (ii) a low level of similarity to a non-CS control profile; and/or (iii) a higher level of similarity to a CS specific control profile than to a non-CS control profile indicates the presence of, or an increased likelihood of, CS.

Another aspect of the disclosure provides a method of assigning a course of management for an individual with CHARGE syndrome (CS), or an increased likelihood of CS, comprising:

-   a) identifying an individual with CS or an increased likelihood of     CS, according to the methods described herein; and -   b) assigning a course of management for CS and/or symptoms of a

CS, comprising i) testing for at least one medical condition associated with CS and ii) applying an appropriate medical intervention based on the results of the testing.

In one embodiment, the medical condition is selected from ophthalmic colobomas, cardiovascular anomalies, hearing loss, airway conditions such as choanal atresia/stenosis or tracheoesophageal fistula, feeding issues, retinal detachment, growth delay, delayed puberty, renal anomalies, developmental difficulties, behavioural problems, dual sensory loss and/or neuropsychological issues such as attention deficit hyperactivity disorder or autism.

Another aspect of the disclosure provides a kit for detecting and/or screening for CHARGE syndrome, or an increased likelihood of CS, in a sample, comprising:

-   a) at least one detection agent for determining the methylation     level of:     -   i) at least 3, optionally at least 5, at least 8, at least 10,         at least 25, at least 44, at least 50, at least 75, at least         100, at least 125, at least 140, or all CpG loci from (i) Tables         2 and/or 16 and/or (ii) associated CpG loci residing within 300         nucleotides, optionally within 150 nucleotides, of the CpG loci         of (i); and/or     -   ii) at least 2, optionally at least 3, at least 4, at least 6,         at least 8, at least 10, at least 16, at least 20, at least 25,         at least 30, at least 35, at least 40, or all the genes from         Tables 2 and/or 16; and -   b) instructions for use.

In an embodiment, the kit further comprises bisulfite conversion reagents, methylation-dependent restriction enzymes, methylation-sensitive restriction enzymes, PCR reagents, probes and/or primers.

In an embodiment, the kit further comprises a computer-readable medium that causes a computer to compare methylation levels from a sample at the selected CpG loci to one or more control profiles and computes a correlation value between the sample and control profile. In an embodiment, the computer readable medium obtains the control profile from historical methylation data for a patient or pool of patients known to have, or not have, CHARGE syndrome. In some embodiments, the computer readable medium causes a computer to update the control profile based on the testing results from the testing of a new patient.

The present disclosure also provides DNA methylation markers which are capable of differentiating Kabuki syndrome (KS) cases carrying a pathogenic KMT2D mutation from non-Kabuki syndrome (non-KS) controls, including distinguishing Kabuki syndrome cases from individuals carrying a benign KMT2D variant (benign variant as referred to herein means a variant in KMT2D gene that does not alter protein function). The DNA methylation markers and the methods of their use described herein may provide useful alternative or supplementary diagnostics to currently available methods of detecting and/or screening for KS, or likelihood of KS.

Accordingly, an aspect of the disclosure provides a method of detecting and/or screening for Kabuki syndrome (KS), or an increased likelihood of KS, in a human subject, comprising:

-   determining a sample methylation profile from a sample comprising     DNA from said subject, said sample profile comprising the     methylation level of at least 6, optionally at least 8, at least 10,     at least 15, at least 20, at least 25, at least 46, at least 50, at     least 75, at least 100, at least 125, at least 150, at least 200, at     least 250, or all CpG loci from (i) Tables 9 and/or 17 and/or (ii)     associated CpG loci residing within 300 nucleotides, optionally     within 150 nucleotides, of the CpG loci of (i); and -   determining the level of similarity of said sample profile to one or     more control profiles, wherein (i) a high level of similarity of the     sample profile to a KS specific control profile; (ii) a low level of     similarity to a non-KS control profile; and/or (iii) a higher level     of similarity to a KS specific control profile than to a non-KS     control profile indicates the presence of, or an increased     likelihood of, KS.

In one embodiment, the selected CpG loci comprise CpG loci from Tables 9 and/or 17 having an absolute KS delta-beta value ≧0.15, optionally ≧0.16, ≧0.18, ≧0.20, ≧0.22, ≧0.24 or ≧0.25; and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i).

Another aspect of the disclosure provides a method of detecting and/or screening for Kabuki syndrome (KS), or an increased likelihood of KS, in a human subject, comprising:

-   determining a sample methylation profile from a sample comprising     DNA from said subject, said sample profile comprising the     methylation level of CpG loci, wherein the CpG loci are the loci     from Tables 9 and/or 17; and -   determining the level of similarity of said sample profile to one or     more control profiles, wherein (i) a high level of similarity of the     sample profile to a KS specific control profile; (ii) a low level of     similarity to a non-KS control profile; and/or (iii) a higher level     of similarity to a KS specific control profile than to a non-KS     control profile indicates the presence of, or an increased     likelihood of, KS.

In one embodiment, the selected CpG loci comprise the CpG loci from Tables 9 and/or 17 having an absolute KS delta-beta value ≧0.16.

In one embodiment, the selected CpG loci comprise the CpG loci from Tables 9 and/or 17 having an absolute KS delta-beta value ≧0.18.

In another embodiment, the selected CpG loci comprise the CpG loci from Tables 9 and/or 17 having an absolute KS delta-beta value ≧0.20.

In another embodiment, the selected CpG loci comprise the CpG loci from Tables 9 and/or 17 having an absolute KS delta-beta value ≧0.22.

In another embodiment, the selected CpG loci comprise the

CpG loci from Tables 9 and/or 17 having an absolute KS delta-beta value ≧0.24.

In another embodiment, the selected CpG loci comprise the CpG loci from Tables 9 and/or 17 having an absolute KS delta-beta value ≧0.25.

In another embodiment, determining the sample methylation profile comprises the steps:

-   a) providing the sample comprising genomic DNA from the subject; -   b) optionally, isolating DNA from the sample; -   c) optionally, treating DNA from the sample with bisulfite for a     time and under conditions sufficient to convert non-methylated     cytosines to uracils; -   d) optionally, amplifying the DNA; and -   e) determining the methylation level at the CpG loci by means of     bisulfite sequencing, pyrosequencing, methylation-sensitive     single-strand conformation analysis (MS-SSCA), high resolution     melting analysis (HRM), combined bisulfite restriction analysis     (COBRA), methylation-sensitive single nucleotide primer extension     (MS-SnuPE), base-specific cleavage/MALDI-TOF, methylation-specific     PCR (MSP), methylation-sensitive restriction enzyme-based methods,     microarray-based methods, whole-genome bisulfite sequencing (WGBS,     MethylC-seq or BS-seq), reduced-representation bisulfite     sequencing(RRBS), and/or enrichment-based methods such as MeDIP-seq,     MBD-seq, or MRE-seq.

In another embodiment, a high level of similarity to the control profile is indicated by a correlation coefficient between the sample profile and the control profile having an absolute value between 0.5 to 1, optionally between 0.75 to 1, and a low level of similarity to the control profile is indicated by a correlation coefficient between the sample profile and the control profile having an absolute value between 0 to 0.5, optionally between 0 to 0.25.

In another embodiment, a higher level of similarity to the KS specific profile than to the non-KS control profile is indicated by a higher correlation value computed between the sample profile and the KS specific profile than an equivalent correlation value computed between the sample profile and the non-KS control profile, optionally wherein the correlation value is a correlation coefficient.

In another embodiment, the correlation coefficient is a linear correlation coefficient, optionally a Pearson correlation coefficient.

In another embodiment, methylation level is measured as a β-value. Optionally, hypermethylation is indicated by the gene having a significantly higher methylation beta value in the KS specific control profile compared to the non-KS control profile and hypomethylation is indicated by the gene having a significantly lower methylation beta value in the KS specific control profile compared to the non-KS control profile.

In another embodiment, determining the profile of methylated DNA from the subject comprises contacting the DNA with at least one agent that provides for determination of a CpG methylation status of at least one, optionally all, of the selected CpG loci, wherein the agent comprises an oligonucleotide-immobilized substrate comprising a plurality of capture probes, each capture probe comprising a pair of capture oligonucleotides, wherein the capture oligonucleotide pairs comprise (a) an oligonucleotide comprising nucleotide sequence complementary to or identical to a nucleotide sequence of genomic DNA comprising a selected CpG loci, and (b) an oligonucleotide comprising nucleotide sequence complementary to or identical to a nucleotide sequence of genomic DNA comprising the same selected CpG loci of (a), in which the cytosine residue of the CpG loci is replaced with a thymine residue.

In another embodiment, the contacting is under hybridizing conditions.

In another embodiment, the methylation levels of the selected loci of at least one control profile is derived from one or more samples, optionally from historical methylation data for a patient or pool of patients.

In another embodiment, the non-KS control profile comprises methylation levels for the selected CpG loci listed in Tables 9 and/or 17.

In another embodiment, the KS specific control profile comprises methylation levels for the selected CpG loci listed in Tables 9 and/or 17.

In another embodiment, the methylation level of a selected CpG locus not listed in Tables 9 and/or 17 is assumed to be equivalent to the methylation level of a CpG locus listed in Tables 9 and/or 17 with which the selected DNA CpG locus is associated.

In another embodiment, the sample is derived from blood, fibroblast tissue, buccal tissue, lymphoblastoid cell line, saliva or a prenatal sample, optionally a CVS, placenta, circulating fetal DNA and/or amniotic fluid sample.

In another embodiment, the human subject is a fetus.

The present disclosure also provides a method of detecting and/or screening for Kabuki syndrome (KS), or an increased likelihood of KS, in a human subject, comprising:

-   determining a sample methylation profile from a sample comprising     DNA from said subject, said sample profile comprising the     methylation level of at least 3, optionally at least 4, at least 6,     at least 8, at least 10, at least 15, at least 20, at least 25, at     least 50, at least 75, at least 100, at least 125, or all the genes     from Tables 9 and/or 17; and -   determining the level of similarity of said sample profile to one or     more control profiles, wherein (i) a high level of similarity of the     sample profile to an KS specific control profile; (ii) a low level     of similarity to a non-KS control profile; and/or (iii) a higher     level of similarity to a KS specific control profile than to a     non-KS control profile indicates the presence of, or an increased     likelihood of, KS.

In one embodiment, the genes are FAM65B, HOXC4 and MYO1F.

In one embodiment, determining the methylation levels of the selected genes comprises the steps:

-   a) providing the sample comprising genomic DNA from the subject; -   b) optionally, isolating DNA from the sample; -   c) optionally, treating DNA from the sample with bisulfite for a     time and under conditions sufficient to convert non-methylated     cytosines to uracils; -   d) optionally, amplifying the DNA; and -   e) determining the methylation status at the selected genes by means     of bisulfite sequencing, pyrosequencing, methylation-sensitive     single-strand conformation analysis (MS-SSCA), high resolution     melting analysis (HRM), combined bisulfite restriction analysis     (COBRA), methylation-sensitive single nucleotide primer extension     (MS-SnuPE), base-specific cleavage/MALDI-TOF, methylation-specific     PCR (MSP), methylation-sensitive restriction enzyme-based methods,     microarray-based methods, whole-genome bisulfite sequencing (WGBS,     MethylC-seq or BS-seq), reduced-representation bisulfite sequencing     (RRBS), and/or enrichment-based methods such as MeDIP-seq, MBD-seq,     or MRE-seq.

In one embodiment, the methylation level is measured as a β-value.

In another embodiment, hypermethylation is indicated by the gene having a significantly higher methylation beta value in the KS specific control profile compared to the non-KS control profile and hypomethylation is indicated by the gene having a significantly lower methylation beta value in the KS specific control profile compared to the non-KS control profile.

In another embodiment, the sample is derived from blood, fibroblast tissue, buccal tissue, lymphoblastoid cell line, saliva or a prenatal sample, optionally a CVS, placenta, circulating fetal DNA and/or amniotic fluid sample.

In another embodiment, the human subject is a fetus.

The present disclosure also provides a method of determining a course of management for an individual with Kabuki syndrome (KS), or an increased likelihood of KS, comprising:

-   a) identifying an individual with KS or an increased likelihood of     KS, according to the methods described herein; and -   b) assigning a course of management for KS and/or symptoms of a KS,     comprising i) testing for at least one medical condition associated     with KS and ii) applying an appropriate medical intervention based     on the results of the testing.

In one embodiment, the medical condition is selected from ophthalmic abnormalities, cardiovascular anomalies, hearing loss, kidney abnormalities, skeletal anomalies, dental abnormalities, feeding difficulties, endocrine problems, infection, autoimmune disorders, seizures and developmental disorders.

The present disclosure further provides a kit for detecting and/or screening for Kabuki syndrome, or an increased likelihood of KS, in a sample, comprising:

-   at least one detection agent for determining the methylation level     of:

at least 6, optionally at least 8, at least 10, at least 15, at least 20, at least 25, at least 46, at least 50, at least 75, at least 100, at least 125, at least 150, at least 200, at least 250, or all CpG loci from (i) Tables 9 and/or 17 and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i); and/or

at least 3, optionally at least 4, at least 6, at least 8, at least 10, at least 15, at least 20, at least 25, at least 50, at least 75, at least 100, at least 125, or all the genes from Tables 9 and/or 17; and instructions for use.

In one embodiment, the kit further comprises bisulfite conversion reagents, methylation-dependent restriction enzymes, methylation-sensitive restriction enzymes, PCR reagents, probes and/or primers.

In another embodiment, the kit further comprises a computer-readable medium that causes a computer to compare methylation levels from a sample at the selected CpG loci to one or more control profiles and compute a correlation value between the sample and control profile.

DRAWINGS

Embodiments are described below in relation to the drawings in which:

FIG. 1 is a volcano plot showing the relationship between the average change in blood DNA methylation in the CHD7 nonsense mutation cohort (n=15; Δβ effect size, X-axis), and the statistical significance of such changes (p-value of the Mann-Whitney U test after Benjamini-Hochberg correction for multiple testing, shown in logarithmic scale, Y-axis). Each semi-transparent point represents one of the 432,601 CpG sites. The horizontal line represents the statistical significance level p<0.01.

FIG. 2 shows hierarchical clustering of 15 CHD7 samples (black; bottom row) and 45 control samples (light grey; top row) from blood.

The clustering was generated from the DNA methylation levels across the 146 CpG sites that exhibited significant changes in methylation (p<0.01 and at least 10% DNAm difference) between the two cohorts. Samples with variants in CHD7 (n=14; dark grey, middle row) were added to the clustering to determine if they clustered with the CHD7 pathogenic variants or with the controls.

FIG. 3 shows the classification of various categories of blood DNA methylation samples. Two median-methylation profiles were built over the 146 significant CpGs: one using the 15 CHD7 nonsense pathogenic mutation samples (filled circles), and another using the 45 Control samples (squares). 1056 normal blood DNAm samples derived from GEO (crosses) were examined, 1051 of which were more similar to the Control profile (specificity>99.5%). 14 samples with variants in CHD7 (triangles) were also classified, of which 9 cases showed a higher similarity to the pathogenic nonsense mutation CHD7 cases and the remaining 5 variants of unknown significance (VUS), three were more similar to the controls. Pearson correlation was used as the similarity metric.

FIG. 4 is a volcano plot showing the relationship between the average change in blood DNA methylation in the Kabuki nonsense mutation cohort compared to normal controls (Δβ effect size, X-axis) and the statistical significance of such changes (p-value of the Mann-Whitney U test after Benjamini-Hochberg correction for multiple testing, shown in logarithmic scale, Y-axis). Each semi-transparent point represents one of the 422,139 CpG sites. The horizontal line represents the statistical significance level p=0.05. The vertical lines represent the effect size of 15% change in DNAm. The data cohorts contained 11 Kabuki nonsense samples and 45 normal controls.

FIG. 5 shows hierarchical clustering of 11 Kabuki samples and 45 control samples from blood. The heatmap shows the clustering based on the DNA methylation levels across the 287 CpG sites that exhibited significant changes in methylation (p<0.05 and at least 15% DNAm difference) between the two cohorts. Samples with variants in KMT2D (n=11) were added to the clustering to determine if they clustered with the Kabuki pathogenic samples or with the controls. Clustering was performed based on the Pearson correlation metric with average linkage (correlation scale shown on the right).

FIG. 6 shows classification of various categories of blood DNA methylation samples. Two median-methylation profiles were built over the 287 significant CpGs: one using the 11 Kabuki samples with pathogenic nonsense mutation in KMT2D (circles), and another using the 45 Control samples (squares). 1056 normal blood DNAm samples derived from GEO (crosses) were also examined, all of which were more similar to the Control profile (specificity=100%). 9 samples were classified with variants in KMT2D, of which 1 case showed a higher similarity to the pathogenic nonsense mutation Kabuki cases and the remaining 8 variants were more similar to the controls. The nine samples all had non-synonymous changes (missense mutations) in KMT2D. Two of these patients had clinical features suggestive of possible-Kabuki syndrome and the remaining seven cases were studied to rule out diagnosis of Kabuki syndrome in children with developmental problems. Pearson correlation was used as the similarity metric.

DESCRIPTION OF VARIOUS EMBODIMENTS

The inventors have conducted genome-wide DNA methylation (DNAm) profiling using blood from individuals with CHARGE syndrome (CS), a disorder involving aberrant CHD7 function. Based on comparison of the DNA methylation profile from CS individuals to those of non-CS controls, the inventors have shown that DNA methylation profiles may be used in a test for early and accurate diagnosis of CHARGE syndrome due to CHD7 pathogenic mutations. 146 CpG loci (Table 2) plus 3 CpG loci (Table 16) were identified as showing a statistically significant (corrected p-value<0.01) difference in methylation levels between CS cases and non-CS controls.

The inventors have also conducted genome-wide DNA methylation (DNAm) profiling using blood from individuals with Kabuki syndrome (KS), a disorder involving aberrant KMT2D function. Based on comparison of the DNA methylation profile from KS individuals to those of non-KS controls, the inventors have shown that DNA methylation profiles may be used in a test for early and accurate diagnosis of Kabuki syndrome due to

KMT2D pathogenic mutations. 287 CpG loci (Table 9) plus 75 CpG loci (Table 17) were identified as showing a statistically significant (corrected p-value≦0.05) difference in methylation levels between KS cases and non-KS controls.

I. Definitions

Terms of degree such as “substantially”, “about” and “approximately” as used herein mean a reasonable amount of deviation of the modified term such that the end result is not significantly changed. These terms of degree should be construed as including a deviation of at least ±5% of the modified term if this deviation would not negate the meaning of the word it modifies or unless the context suggests otherwise to a person skilled in the art.

As used herein, the term “isolated” or “purified” when used in relation to a DNA molecule refers to a DNA molecule that is extracted and separated from one or more contaminants with which it naturally occurs.

As used herein, “methylation” refers specifically to DNA methylation, and more particularly to a modification in which a methyl group or hydroxymethyl group is added to the 5 position of a cytosine residue to form a 5-methyl cytosine (5-mCyt) or 5-hydroxymethylcytosine (5-hmC).

As used herein, “CpG locus” or “methylation locus” refers to an individual CpG dinucleotide sequence in genomic DNA which is capable of being methylated. Individual CpG loci may be identified by reference to an Illumina CpG locus (Illumina ID #) which is defined by a chromosome number, genomic coordinate (referenced to NCBI, hg19), genome build (37), and +/−strand designation to unambiguously define each CpG locus. The genomic information is publically available through the UCSC genome browser at https://cienome.ucsc.edu/.

The term “methylation level” refers to a measure of the amount of methylation at a target site (for example, a CpG locus) within a DNA molecule in a sample. For example, the level of methylation can be measured for one or more CpG dinucleotides, or for a region of DNA. If the methylation level of a target site within a sample is higher than a reference level, the sample is considered to have increased methylation relative to the reference at the target site. Conversely, if the methylation level of a target site within a sample is lower than the reference level, the sample is considered to have a decreased methylation level relative to the reference at the target site. The target site may be an individual CpG locus or a region of DNA comprising multiple CpG loci, for example, a gene promoter. Methylation levels of a target site may be measured by methods known in the art, for example, as a “β value” or “beta value”, which is calculated as:

β value=intensity of the methylated target (M)/(intensity of the unmethylated target (U)+intensity of the methylated target(M)+100)

A β value of zero indicates no methylation and a value of one indicates 100% methylation.

As used herein, the term “methylation status” refers to whether a specified target DNA site is methylated or not methylated. The target site may be an individual CpG locus or a region of DNA comprising multiple CpG loci, for example, a gene promoter. For example, a target site may have a methylation status of “methylated” or “hypermethylated” if the target has significantly higher methylation beta value in a CS (or KS) specific control profile compared to a non-CS (or non-KS) control profile. Conversely, a target site may have a methylation status of “not methylated” or “hypomethylated” if the target has significantly lower methylation beta value in a CS (or KS) specific control profile compared to a non-CS (or non-KS) control profile.

As used herein, the term “delta beta” or “delta β” refers to the difference between the β value of a methylation target in two different samples, for example, the β value of a methylation target in a CS (or KS) specific control profile and the β value of the same methylation target in a non-CS (or non-KS) control profile.

As used herein the term “gene” refers to a genomic DNA sequence that comprises a coding sequence associated with the production of a polypeptide or polynucleotide product (e.g., rRNA, tRNA). The methylation level of a gene as used herein, encompasses the methylation level of sequences which are known or predicted to affect expression of the gene, including the promoter, enhancer, and transcription factor binding sites. As used herein, the term “enhancer” refers to a cis-acting region of DNA that is located up to 1 Mbp (upstream or downstream) of a gene.

As used herein, the term “sample methylation profile” or “sample profile” refers to the methylation levels at one or more target sequences in a subject's genomic DNA. The target sequence may be an individual CpG locus or a region of DNA comprising multiple CpG loci, for example, a gene promoter or CpG island. The methylation profile of a sample tested according the methods disclosed herein is referred to as a sample profile.

In some embodiments, the sample methylation profile is compared to one or more control profiles. The control profile may be a reference value and/or may be derived from one or more samples, optionally from historical methylation data for a patient or pool of patients who are known to have, or not have, CHARGE syndrome or Kabuki syndrome. In such cases, the historical methylation data can be a value that is continually updated as further samples are collected and individuals are identified as CS or not-CS, or KS or not-KS. It will be understood that the control profile represents an average of the methylation levels for selected CpG loci as described herein. Average methylation values may, for example, be the mean values or median values.

For example, a “CS specific control profile” or “CS control profile” may be generated by measuring the methylation levels at specified target sequences in genomic DNA from an individual subject, or population of subjects, who are known to have CS and a CHD7 pathogenic mutation.

Similarly, a “non-CS control profile” may be generated by measuring the methylation levels at specified target sequences in genomic DNA from an individual subject or population of subjects who are known to not have CS.

In another example, a “KS specific control profile” or “KS control profile” may be generated by measuring the methylation levels at specified target sequences in genomic DNA from an individual subject, or population of subjects, who are known to have KS and a KMT2D pathogenic mutation. Similarly, a “non-KS control profile” may be generated by measuring the methylation levels at specified target sequences in genomic DNA from an individual subject or population of subjects who are known to not have KS.

In certain embodiments, the tissue source from which the sample profile and control profile are derived is matched, so that they are both derived from the same or similar tissue.

As used herein, the phrase “detecting and/or screening” for a condition refers to a method or process of determining if a subject has or does not have said condition. Where the condition is a likelihood or risk for a disease or disorder, the phrase “detecting and/or screening” will be understood to refer to a method or process of determining if a subject is at an increased or decreased likelihood for the disease or disorder.

As used herein, the term “sensitivity” refers to the ability of the test to correctly identify those patients with the disease or disorder, such that a 100% sensitivity indicates a test that correctly identifies all patients with the disease or disorder. Sensitivity is calculated as:

Sensitivity=(True Positives)/(True Positives+False Negatives). A high sensitivity as used herein refers to a sensitivity of greater than 50%.

As used herein, the term “specificity” refers to the ability of a test to correctly identify those patients without the disease or disorder, such that a 100% specificity indicates a test that correctly identifies all patients without the disease or disorder. Specificity is calculated as:

Specificity=(True Negatives)/(True Negatives+False Positives). A high specificity as used herein refers to a specificity of greater than 50%.

As used herein, the term “CpG” or “CG” site refers to cytosine and guanosine residues located sequentially (5′->3′) in a polynucleotide DNA sequence. The term “CpG island” refers to a region of genomic DNA characterized by a high frequency of CpG sites, for example, a CpG island may be characterized by CpG dinucleotide content of at least 60% over the length of the island. As used herein the term “CpG island shore” refers to a region of DNA occurring within 2 kbp (upstream or downstream) of a CpG island. As used herein the term “body” (in reference to a gene) refers to the genomic region covering the entire gene from the transcription start site to the end of the transcript. As used herein the term “distance from TSS” refers to the genomic difference in base pairs between specific CpG locus and the nearest transcription start site.

As used herein, a first CpG locus is “associated” with a second CpG locus, if the methylation status at the first locus is reasonably predictive of the methylation status of the second locus and vice versa. CpG loci may be considered “associated”, for example, if they occur within the same CpG island, CpG island shore, gene promoter or gene enhancer region. CpG loci may also be considered “associated” by virtue of their genomic proximity, for example, CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of each other may be considered associated.

As used herein, the term “treating DNA from the sample with bisulfite” refers to treatment of DNA with a reagent comprising bisulfite, disulfite, hydrogen sulfite or combinations thereof, for a time and under conditions sufficient to convert unmethylated DNA cytosine residues to uracil, thereby facilitating the identification of methylated and unmethylated CpG dinucleotide sequences. Bisulfite modifications to DNA may be detected according to methods known in the art, for example, using sequencing or detection probes which are capable of discerning the presence of a cytosine or uracil residue at the CpG site.

The term “subject” as used herein refers to a human subject and includes, for example, a fetus.

The terms “complementary” or “complementarity” are used in reference to a first polynucleotide (which may be an oligonucleotide) which is in “antiparallel association” with a second polynucleotide (which also may be an oligonucleotide). As used herein, the term “antiparallel association” refers to the alignment of two polynucleotides such that individual nucleotides or bases of the two associated polynucleotides are paired substantially in accordance with Watson-Crick base-pairing rules. Complementarity may be “partial,” in which only some of the polynucleotides' bases are matched according to the base pairing rules. Or, there may be “complete” or “total” complementarity between the polynucleotides. Those skilled in the art of nucleic acid technology can determine duplex stability empirically by considering a number of variables, including, for example, the length of the first polynucleotide, which may be an oligonucleotide, the base composition and sequence of the first polynucleotide, and the ionic strength and incidence of mismatched base pairs.

The term “hybridize” refers to the sequence specific non-covalent binding interaction with a complementary nucleic acid. Appropriate stringency conditions which promote hybridization are known to those skilled in the art, or can be found in Current Protocols in Molecular Biology, John Wiley & Sons, N.Y. (1989), 6.3.1 6.3.6. For example, 6.0×sodium chloride/sodium citrate (SSC) at about 45° C. for 15 minutes, followed by a wash of 2.0×SSC at 50° C. for 15 minutes may be employed.

The stringency may be selected based on the conditions used in the wash step. For example, the salt concentration in the wash step can be selected from a high stringency of about 0.2×SSC at 50° C. for 15 minutes. In addition, the temperature in the wash step can be at high stringency conditions, at about 65° C. for 15 minutes.

By “at least moderately stringent hybridization conditions” it is meant that conditions are selected which promote selective hybridization between two complementary nucleic acid molecules in solution. Hybridization may occur to all or a portion of a nucleic acid sequence molecule. The hybridizing portion is typically at least 15 (e.g. 20, 25, 30, 40 or 50) nucleotides in length. Those skilled in the art will recognize that the stability of a nucleic acid duplex, or hybrids, is determined by the Tm, which in sodium containing buffers is a function of the sodium ion concentration and temperature (Tm=81.5° C.−16.6 (Log10[Na+])+0.41(%(G+C)−600/l), or similar equation). Accordingly, the parameters in the wash conditions that determine hybrid stability are sodium ion concentration and temperature. In order to identify molecules that are similar, but not identical, to a known nucleic acid molecule a 1% mismatch may be assumed to result in about a 1° C. decrease in Tm, for example if nucleic acid molecules are sought that have a >95% sequence identity, the final wash temperature will be reduced by about 5° C. Based on these considerations those skilled in the art will be able to readily select appropriate hybridization conditions. In an embodiment, stringent hybridization conditions are selected. By way of example the following conditions may be employed to achieve stringent hybridization: hybridization at 5×sodium chloride/sodium citrate (SSC)/5×Denhardt's solution/1.0% SDS at Tm −5° C. based on the above equation, followed by a wash of 0.2×SSC/0.1% SDS at 60° C. for 15 minutes. Moderately stringent hybridization conditions include a washing step in 3×SSC at 42° C. for 15 minutes. It is understood, however, that equivalent stringencies may be achieved using alternative buffers, salts and temperatures. Additional guidance regarding hybridization conditions may be found in: Current Protocols in Molecular Biology, John Wiley & Sons, N.Y., 1989, 6.3.1-6.3.6 and in: Sambrook et al., Molecular Cloning, a Laboratory Manual, Cold Spring Harbor Laboratory Press, 2000, Third Edition.

The term “oligonucleotide” as used herein refers to a nucleic acid substantially free of cellular material or culture medium when produced by recombinant DNA techniques, or chemical precursors, or other chemicals when chemically synthesized. The term “nucleic acid” and/or “oligonucleotide” as used herein refers to a sequence of nucleotide or nucleoside monomers consisting of naturally occurring bases, sugars, and intersugar (backbone) linkages, and is intended to include DNA and RNA which can be either double stranded or single stranded, represent the sense or antisense strand. The term also includes modified or substituted oligomers comprising non-naturally occurring monomers or portions thereof.

As used herein, the term “amplify”, “amplifying” or “amplification” of DNA refers to the process of generating at least one copy of a DNA molecule or portion thereof. Methods of amplification of DNA are well known in the art, including but not limited to polymerase chain reaction (PCR), ligase chain reaction (LCR), self-sustained sequence replication (3SR), nucleic acid sequence based amplification (NASBA), strand displacement amplification (SDA), multiple displacement amplification (MDA) and rolling circle amplification (RCA).

II. Methods

As set out in Table 2, the instant disclosure identifies 146 distinct CpG loci, each of which show a statistically significant (corrected p-value<0.01) difference in methylation levels between individuals with CS and non-CS controls over the tested population. As set out in Table 16, the instant disclosure identifies an additional 3 CpG loci, each of which show as statistically significant (corrected p-value<0.01) difference in methylation levels between individuals with CS and non-CS controls over the tested population. As described in the Examples, the methylation levels of the disclosed loci, or a subset thereof, may be used in diagnostic testing for CS, with up to 100% sensitivity and specificity. It will be understood that the sensitivity and specificity of the methods described will tend to increase with the number of CpG loci or sites selected for testing (i.e. the size of the signature), to a maximal sensitivity/specificity of 100%. However, signatures utilizing fewer CpG loci, are described herein which retain greater than 50% sensitivity and specificity and are useful for assessing likelihood of CHARGE syndrome.

Further, as set out in Table 9, the instant disclosure identifies 287 distinct CpG loci, each of which show a statistically significant (corrected p-value≦0.05) difference in methylation levels between individuals with KS and non-KS controls over the tested population. Also, as set out in Table 17, the instant disclosure identifies and additional 75 distinct CpG loci, each of which show a statistically significant (corrected p-value≦0.05) difference in methylation levels between individuals with KS and non-KS controls over the tested population. As described in the Examples, the methylation levels of the disclosed loci, or a subset thereof, may be used in diagnostic testing for KS, with up to 100% sensitivity and specificity. It will be understood that the sensitivity and specificity of the methods described will tend to increase with the number of CpG loci or sites selected for testing (i.e. the size of the signature), to a maximal sensitivity/specificity of 100%. However, signatures utilizing fewer CpG loci, are described herein which retain greater than 50% sensitivity and specificity and are useful for assessing likelihood of Kabuki syndrome.

Useful methylation signatures according to the described methods are not intended to be limited to the sites of Table 2, Table 16, Table 9 and Table 17, but are intended to include associated CpG loci, and associated gene and non-gene regions. DNA methylation at a single CpG locus can predict DNA methylation of multiple other loci residing in near genomic proximity or overlapping CpG islands. Accordingly, “associated” loci and regions are loci and regions, the methylation levels or status of which may be reasonably predicted by the methylation levels or status of one or more of the CpG loci of Table 2, Table 16, Table 9 and Table 17. CpG loci may be considered “associated”, for example, if they occur within the same CpG island, CpG island shore, gene promoter or gene enhancer region. CpG loci may also be considered “associated” by virtue of their proximity, for example, CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of each other may be considered associated.

Accordingly, an aspect of the disclosure provides a method of detecting and/or screening for CHARGE syndrome (CS), or an increased likelihood of CS, in a human subject, comprising determining a sample methylation profile from a sample of DNA from said subject, said sample profile comprising the methylation level of at least 3, optionally at least 5, at least 8, at least 10, at least 25, at least 44, at least 50, at least 75, at least 100, at least 125, at least 140, or all CpG loci from (i) Tables 2 and/or 16 and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i).

Another aspect of the disclosure provides a method of detecting and/or screening for Kabuki syndrome (KS), or an increased likelihood of KS, in a human subject, comprising determining a sample methylation profile from a sample of DNA from said subject, said sample profile comprising the methylation level of at least 6, optionally at least 8, at least 10, at least 15, at least 20, at least 25, at least 46, at least 50, at least 75, at least 100, at least 125, at least 150, at least 200, at least 250, or all CpG loci from (i) Tables 9 and/or 17 and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i).

Methods of DNA methylation profiling of target genomic regions are generally known in the art (Stevens et al 2013, Harris et al 2010 and Hirst 2013).

For example, a non-limiting list of exemplary methods that may be used to determine methylation levels at a specified target sequence of

DNA include: bisulfite sequencing, pyrosequencing, methylation-sensitive single-strand conformation analysis (MS-SSCA), high resolution melting analysis (HRM), methylation-sensitive single nucleotide primer extension (MS-SnuPE), base-specific cleavage/MALDI-TOF, methylation-specific PCR (MSP), methylation-sensitive restriction enzyme-based methods and/or microarray-based methods.

In an embodiment, methylation levels are measured using an agent that provides for determination of a CpG methylation status of at least one, optionally all, of the selected CpG loci, wherein the agent comprises an oligonucleotide-immobilized substrate comprising a plurality of capture probes, each capture probe comprising a pair of capture oligonucleotides, wherein the capture oligonucleotide pairs comprise (a) an oligonucleotide comprising nucleotide sequence complementary to or identical to a nucleotide sequence of genomic DNA comprising a selected CpG loci, and (b) an oligonucleotide comprising nucleotide sequence complementary to or identical to a nucleotide sequence of genomic DNA comprising the same selected CpG loci of (a), in which the cytosine residue of the CpG loci is replaced with a thymine residue. A non-limiting example of such an agent includes a “microarray”, comprising an ordered set of probes fixed to a solid surface that permits analysis such as methylation analysis of a plurality of genomic targets sequences.

According to the methods described herein, similarity of the DNA methylation profile from a sample to one or more control profiles, may be used to identify individuals having CHARGE syndrome, or an increased likelihood of having CHARGE syndrome. For example, in an embodiment, the method comprises determining the level of similarity of a sample profile to one or more control profiles, wherein (i) a high level of similarity of the sample profile to a CS specific profile; (ii) a low level of similarity to a non-CS control profile; and/or (iii) a higher level of similarity to a CS specific profile than to a non-CS control profile indicates the presence of, or an increased likelihood of, CS.

Similarity of the DNA methylation profile from a sample to one or more control profiles, may also be used to identify individuals having Kabuki syndrome, or an increased likelihood of having Kabuki syndrome. For example, in an embodiment, the method comprises determining the level of similarity of a sample profile to one or more control profiles, wherein (i) a high level of similarity of the sample profile to a KS specific profile; (ii) a low level of similarity to a non-KS control profile; and/or (iii) a higher level of similarity to a KS specific profile than to a non-KS control profile indicates the presence of, or an increased likelihood of, KS.

It will be appreciated that the control profile may be a reference value, or derived from one or more samples, optionally from historical methylation data for a patient or pool of patients. The control profile may be a reference value and/or may be derived from one or more samples, optionally from historical methylation data for a patient or pool of patients who are known to have, or not have, CHARGE syndrome and/or Kabuki syndrome. In such cases, the historical methylation data can be a value that is continually updated as further samples are collected and individuals are identified as CS or not-CS, or KS or not-KS. For example, the control database may be stored on an online database, which is continually updated with methylation data from diagnosed CS and non-CS patients and diagnosed KS and non-KS patients. It will be understood that the control profile represents an average of the methylation levels for selected CpG loci as described herein.

In an embodiment, the “CS specific control profile” is generated by measuring the methylation levels at specified target sequences in genomic DNA from an individual subject, or population of subjects, who are known to have CS. Similarly, in an embodiment, the “non-CS control profile” is generated by measuring the methylation levels at specified target sequences in genomic DNA from an individual subject, or population of subjects, who are known to not have CS. In certain embodiments, the tissue source from which the sample profile and control profile are derived is matched, so that they are both derived from the same or similar tissue. In other embodiments, the sample profile and control profile are derived from different tissues. In certain other embodiments, the CS specific control profile and the non-CS control profile are derived from historical data and can indicate similarity of a sample to either the CS or non-CS profiles.

In another embodiment, the “KS specific control profile” is generated by measuring the methylation levels at specified target sequences in genomic DNA from an individual subject, or population of subjects, who are known to have KS. Similarly, in an embodiment, the “non-KS control profile” is generated by measuring the methylation levels at specified target sequences in genomic DNA from an individual subject, or population of subjects, who are known to not have KS. In certain embodiments, the tissue source from which the sample profile and control profile are derived is matched, so that they are both derived from the same or similar tissue. In other embodiments, the sample profile and control profile are derived from different tissues. In certain other embodiments, the KS specific control profile and the non-KS control profile are derived from historical data and can indicate similarity of a sample to either the KS or non-KS profiles.

Methods of determining the similarity between methylation profiles are well known in the art. Methods of determining similarity may in some embodiments provide a non-quantitative measure of similarity, for example, using visual clustering. In another embodiment, similarity may be determined using methods which provide a quantitative measure of similarity.

For example, in an embodiment, similarity may be measured using hierarchical clustering, optionally using Manhattan distance. For example, unsupervised hierarchical clustering of a sample with a CS specific control profile indicates similarity to the CS specific control profile. Likewise, unsupervised hierarchical clustering of a sample with a non-CS control profile indicates similarity to the non-CS control profile. In another example, unsupervised hierarchical clustering of a sample with a KS specific control profile indicates similarity to the KS specific control profile. Likewise, unsupervised hierarchical clustering of a sample with a non-KS control profile indicates similarity to the non-KS control profile.

The Manhattan distance function computes the distance that would be traveled to get from one data point to the other if a grid-like path is followed. The Manhattan distance between two items is the sum of the differences of their corresponding components.

The formula for this distance between a point X=(X1, X2, etc.) and a point Y=(Y1, Y2, etc.) is:

$d = {\sum\limits_{i = 1}^{n}{{x_{i} - y_{i}}}}$

Where n is the number of variables, and Xi and Yi are the values of the variable, at points X and Y respectively.

In another embodiment, similarity may be measured by computing a “correlation coefficient”, which is a measure of the interdependence of random variables that ranges in value from −1 to +1, indicating perfect negative correlation at −1, absence of correlation at zero, and perfect positive correlation at +1. In an embodiment, the correlation coefficient may be a linear correlation coefficient, for example, a Pearson product-moment correlation coefficient.

A Pearson correlation coefficient (r) is calculated using the following formula:

$\text{?}\text{?}\frac{\sum\limits_{i}{\left( {\text{?} - \text{?}} \right)\left( {\text{?} - \overset{\_}{y}} \right)}}{\sqrt{\sum\limits_{i}\left( {\text{?} - \text{?}} \right)^{2}}\sqrt{\sum\limits_{i}\left( {\text{?} - \overset{\_}{y}} \right)^{2}}}$ ?indicates text missing or illegible when filed

In one embodiment, x and y are the beta values for various CpG loci in a sample profile and a control profile, respectively.

In an embodiment, a correlation coefficient calculated between the sample profile and the control profile indicates a high level of similarity to the control profile when the correlation coefficient has an absolute value between 0.5 to 1, optionally between 0.75 to 1, and a low level of similarity to the control profile when the correlation coefficient has an absolute value between 0 to 0.5, optionally between 0 to 0.25.

It will be appreciated that any “correlation value” which provides a quantitative scaling measure of similarity between methylation profiles may be used to measure similarity. A sample profile may be identified as belonging to an individual with CS, or an increased likelihood of CS, where the sample profile has high similarity to the CS profile, low similarity to the non-CS profile, or higher similarity to the CS profile than to the non-CS profile. Conversely, a sample profile may be identified as belonging to an individual without CS, or a decreased likelihood of CS, where the sample profile has high similarity to the non-CS profile, low similarity to the CS profile, or higher similarity to the non-CS profile than to the CS profile.

For example, in an embodiment, a sample profile may be identified as belonging to an individual with CS, or an increased likelihood of CS, based on calculation of a CHARGE Syndrome Score, which generally is defined by the following formula:

CS score(B)=r (B, CS profile)−r (B, control profile)

where r is the Pearson correlation coefficient, and B is a vector of DNA methylation levels across the selected CpG loci.

A sample profile with a positive CHARGE Syndrome Score is more similar to the CS specific profile across the selected CpG loci, and is therefore classified as “CS”; whereas a sample with a negative CHARGE Syndrome Score is more similar to the non-CS profile across the selected CpG loci, and is classified as “not CS”.

In another embodiment, a sample profile may be identified as belonging to an individual with KS, or an increased likelihood of KS, where the sample profile has high similarity to the KS profile, low similarity to the non-KS profile, or higher similarity to the KS profile than to the non-KS profile. Conversely, a sample profile may be identified as belonging to an individual without KS, or a decreased likelihood of KS, where the sample profile has high similarity to the non-KS profile, low similarity to the KS profile, or higher similarity to the non-KS profile than to the KS profile.

For example, in an embodiment, a sample profile may be identified as belonging to an individual with KS, or an increased likelihood of KS, based on calculation of a Kabuki Syndrome Score, which generally is defined by the following formula:

KS score(B)=r (B, KS profile)−r (B, control profile)

where r is the Pearson correlation coefficient, and B is a vector of DNA methylation levels across the selected CpG loci.

A sample profile with a positive Kabuki Syndrome Score is more similar to the KS specific profile across the selected CpG loci, and is therefore classified as “KS”; whereas a sample with a negative Kabuki Syndrome Score is more similar to the non-KS profile across the selected CpG loci, and is classified as “not KS”.

As used herein the term “sample” refers to a biological sample comprising genomic DNA from a human subject. The sample may, for example, comprise blood, fibroblast tissue, buccal tissue, and/or amniotic fluid.

Median methylation levels for CS and non-CS cases reported in Tables 2 and/or 16 and for KS and non-KS reported in Tables 9 and/or 17 were identified using whole blood samples. Based on DNA methylation profiles in other disorders with mutations in epigenes, it is predicted that the

DNA methylation profile for CS and non-CS syndrome, and KS and non-KS, can be present in other samples, for example, fibroblast tissue, buccal tissue, lymphoblastoid cell lines, saliva or a prenatal sample. The prenatal sample is optionally a CVS, placenta, circulating fetal DNA and/or amniotic fluid sample.

Another aspect provides a method of detecting and/or screening for CHARGE syndrome (CS), or an increased likelihood of CS, in a human subject, comprising determining a sample DNA methylation profile from a sample of DNA from said subject, said sample profile comprising the methylation level of at least 2, optionally at least 3, at least 4, at least 6, at least 8, at least 10, at least 16, at least 20, at least 25, at least 30, at least 35, at least 40, or all the genes from Tables 2 and/or 16.

The method further comprises determining the level of similarity of said sample profile to one or more control profiles, wherein (i) a high level of similarity of the sample profile to a CS specific control profile; (ii) a low level of similarity to a non-CS control profile; and/or (iii) a higher level of similarity to a CS specific control profile than to a non-CS control profile indicates the presence of, or an increased likelihood of, CS.

Yet another aspect provides a method of detecting and/or screening for Kabuki syndrome (KS), or an increased likelihood of KS, in a human subject, comprising determining a sample DNA methylation profile from a sample of DNA from said subject, said sample profile comprising the methylation level of at least 3, optionally at least 4, at least 6, at least 8, at least 10, at least 15, at least 20, at least 25, at least 50, at least 75, at least 100, at least 125, or all the genes from Tables 9 and/or 17.

In one embodiment, the genes are FAM65B, HOXC4 and MYO1F. It is shown in Table 15, for example, that at an absolute delta beta of 0.25 and p-value 0.00001, the three genes FAM65B, HOXC4 and MYO1F provide a specificity of 100% and a sensitivity of 90.9%.

The method further comprises determining the level of similarity of said sample profile to one or more control profiles, wherein (i) a high level of similarity of the sample profile to a KS specific control profile; (ii) a low level of similarity to a non-KS control profile; and/or (iii) a higher level of similarity to a KS specific control profile than to a non-KS control profile indicates the presence of, or an increased likelihood of, KS.

It will also be appreciated by a person of skill in the art that the methods described herein can be used to distinguish between CHARGE syndrome and other neurodevelopmental syndromes such as Kabuki syndrome. Further, the methods described herein can be used to distinguish between Kabuki syndrome and other neurodevelopmental syndromes such as CHARGE syndrome.

While both CHARGE syndrome and Kabuki syndrome share some characteristics such as developmental delay, cardiovascular malformations, growth deficiency, orofacial clefts, genitourinary anomalies, including cryptorchidism in males, seizures and hearing loss (there can be different causes for each condition), there are also clinical characteristics that are typical of CHARGE syndrome and not Kabuki syndrome and vice versa.

For example, clinical characteristics typical of CHARGE Syndrome, but not Kabuki syndrome, include, but are not limited to: unilateral or bilateral coloboma of the iris, retina-choroid, and/or disc with or without microphthalmos (80%-90% of individuals); unilateral or bilateral choanal atresia or stenosis (50%-60%); cranial nerve dysfunction resulting in hyposmia or anosmia, unilateral or bilateral facial palsy (40%), impaired hearing, and/or swallowing problems (70%-90%); and abnormal outer ears, ossicular malformations, Mondini defect of the cochlea and absent or hypoplastic semicircular canals (>90%).

Further, clinical characteristics typical of Kabuki Syndrome, but not CHARGE syndrome, include, but are not limited to: skeletal anomalies; spinal column abnormalities, including sagittal cleft vertebrae, butterfly vertebrae, narrow intervertebral disc space, and/or scoliosis; hypodontia;

susceptibility to infections and autoimmune disorders; gastrointestinal anomalies, including anal atresia; and ophthalmologic anomalies, including ptosis and strabismus.

Therefore, a proper diagnosis of CHARGE syndrome or Kabuki syndrome allows for testing, treatment and medical management appropriate for each condition, given the differences in their clinical characteristics.

Accordingly, the present disclosure provides a method of detecting and/or screening for CHARGE syndrome (CS) or Kabuki syndrome (KS), or an increased likelihood of CS or KS, in a human subject, comprising:

-   determining a sample methylation profile from a sample comprising     DNA from said subject, said sample profile comprising (a) the     methylation level of at least 3, optionally at least 5, at least 8,     at least 10, at least 25, at least 44, at least 50, at least 75, at     least 100, at least 125, at least 140, or all

CpG loci from (i) Tables 2 and/or 16 and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i); and (b) the methylation level of at least 6, optionally at least 8, at least 10, at least 15, at least 20, at least 25, at least 46, at least 50, at least 75, at least 100, at least 125, at least 150, at least 200, at least 250, or all CpG loci from (i) Tables 9 and/or 17 and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i); and

-   determining the level of similarity of said sample profile to one or     more control profiles, wherein (i) a high level of similarity of the     sample profile to a CS specific control profile; (ii) a low level of     similarity to a KS specific control profile; and/or (iii) a higher     level of similarity to a CS specific control profile than to a KS     specific control profile indicates the presence of, or an increased     likelihood of, CS and/or wherein (i) a high level of similarity of     the sample profile to a KS specific control profile; (ii) a low     level of similarity to a CS specific control profile; and/or (iii) a     higher level of similarity to a KS specific control profile than to     a CS specific control profile indicates the presence of, or an     increased likelihood of, KS.

The disclosure also provides a method of distinguishing between CHARGE syndrome (CS) or Kabuki syndrome (KS), or an increased likelihood of CS or KS, in a human subject, comprising:

-   (A) determining a sample methylation profile from a sample     comprising DNA from said subject, said sample profile comprising the     methylation level of at least 3, optionally at least 5, at least 8,     at least 10, at least 25, at least 44, at least 50, at least 75, at     least 100, at least 125, at least 140, or all CpG loci from (i)     Tables 2 and/or 16 and/or (ii) associated CpG loci residing within     300 nucleotides, optionally within 150 nucleotides, of the CpG loci     of (i); and -   determining the level of similarity of said sample profile to one or     more control profiles, wherein (i) a high level of similarity of the     sample profile to a CS specific control profile; (ii) a low level of     similarity to a non-CS control profile; and/or (iii) a higher level     of similarity to a CS specific control profile than to a non-CS     control profile indicates the presence of, or an increased     likelihood of, CS, and -   (B) determining a second sample methylation profile from a sample     comprising DNA from said subject, said second sample profile     comprising the methylation level of at least 6, optionally at least     8, at least 10, at least 15, at least 20, at least 25, at least 46,     at least 50, at least 75, at least 100, at least 125, at least 150,     at least 200, at least 250, or all CpG loci from (i) Tables 9 and/or     17 and/or (ii) associated CpG loci residing within 300 nucleotides,     optionally within 150 nucleotides, of the CpG loci of (i); and -   determining the level of similarity of said second sample profile to     one or more control profiles, wherein (i) a high level of similarity     of the second sample profile to a KS specific control profile; (ii)     a low level of similarity to a non-KS control profile; and/or (iii)     a higher level of similarity to a KS specific control profile than     to a non-KS control profile indicates the presence of, or an     increased likelihood of, KS.

Confirmation of a diagnosis of CHARGE aids in medical management by enabling targeted screening for the multisystem manifestations of this complex condition, optimizing the opportunity for early intervention and management. Recommended evaluations following a diagnosis include: ophthalmology exam to look for colobomas, cardiac exam to screen for cardiovascular anomalies, audiology exam to assess for hearing loss, airway evaluation (risk for choanal atresia/stenosis and tracheoesophageal fistula) and feeding evaluation (aspiration/swallowing dysfunction common due to abnormalities of cranial nerve IX/X). Individuals with CHARGE syndrome will require ongoing ophthalmology follow-up, as they may have an increased risk for retinal detachment, and audiology follow-up for management of hearing loss. Individuals with CHARGE syndrome should be followed by endocrinology as growth delay is usually evident by late infancy and may require investigation/management. In addition individuals with CHARGE syndrome are at increased risk for delayed puberty as a result of hypogonadotropic hypogonadism for which they require ongoing monitoring. In light of the increased risk of renal anomalies, a renal ultrasound should be done. In addition, neuropsychological assessment to screen for developmental difficulties (highly prevalent) and behavioural problems (e.g. aggression, obsessive-compulsive behaviors) provides the opportunity for early identification and intervention. Individuals with CHARGE syndrome are at increased risk for dual sensory loss (hearing and vision). There is also an increased risk for other neuropsychological issues including attention deficit hyperactivity disorder and autism—early diagnosis provides the opportunity for early intervention and improved outcomes. Early identification of the above medical and cognitive issues provides the opportunity for an enhanced quality of life for individuals with CHARGE syndrome.

Similarly, confirmation of a diagnosis of Kabuki syndrome aids in medical management by enabling targeted screening for the multisystem manifestations of this complex condition, optimizing the opportunity for early intervention and management. Recommended evaluations following a diagnosis include: ophthalmology exam to look for strabisimus and ptosis, cardiac exam to screen for cardiovascular anomalies, audiology exam to assess for hearing loss, abdominal ultrasound to screen for kidney abnormalities, x-rays for skeletal anomalies, dental assessment for missing teeth and feeding evaluation for gastrosophageal reflux and gastrostomy tube placement if feeding difficulties are severe. Prophylactic antibiotic treatment prior to and during any procedure (e.g. dental work) may be indicated for those with specific heart defects. Individuals with Kabuki syndrome will require ongoing endocrine assessment for various endocrine problems including isolated premature thelarche, ophthalmology follow-up if strabismus or ptosis are present, and audiology follow-up for management of hearing loss. In addition, individuals with Kabuki syndrome require ongoing follow-up for their increased risks for infections and autoimmune disorders as well as seizures In addition, neuropsychological assessment to screen for developmental difficulties (highly prevalent) and autism provides the opportunity for early identification and intervention. Early identification of the above medical and cognitive issues provides the opportunity for an enhanced quality of life for individuals with Kabuki syndrome.

Accordingly, an aspect of the disclosure provides a method of assigning a course of management for an individual with CHARGE syndrome (CS), or an increased likelihood of CS, comprising:

-   a) identifying an individual with CS or an increased likelihood of     CS, according to the methods described herein; and -   b) assigning a course of management for CS and/or symptoms of CS,     comprising i) testing for at least one medical condition associated     with CS and ii) applying an appropriate medical intervention based     on the results of the testing.

Another aspect of the disclosure provides a method of assigning a course of management for an individual with Kabuki syndrome (KS), or an increased likelihood of KS, comprising:

-   a) identifying an individual with KS or an increased likelihood of     KS, according to the methods described herein; and -   b) assigning a course of management for KS and/or symptoms of KS,     comprising i) testing for at least one medical condition associated     with KS and ii) applying an appropriate medical intervention based     on the results of the testing.

As used herein, the term “a course of management” refers to the any testing, treatment, medical intervention and/or therapy applied to an individual with CS or KS and/or symptoms of CS or KS. Medical interventions include, but are not limited to, pharmaceutical treatments, surgical procedures, utilization of medical devices such as hearing aids or glasses, physical or occupational therapy and behavioral or cognitive therapy.

In one embodiment, the medical condition associated with CS is selected from ophthalmic colobomas, cardiovascular anomalies, hearing loss, airway conditions such as choanal atresia/stenosis or tracheoesophageal fistula, feeding issues, retinal detachment, growth delay, delayed puberty, renal anomalies, developmental difficulties, behavioural problems, dual sensory loss and neuropsychological issues such as attention deficit hyperactivity disorder or autism. Other medical conditions associated with CS include, but are not limited to, developmental delay, cardiovascular malformations, growth deficiency, orofacial clefts, genitourinary anomalies, including cryptorchidism in males, seizures and hearing loss, unilateral or bilateral coloboma of the iris, retina-choroid, and/or disc with or without microphthalmos, unilateral or bilateral choanal atresia or stenosis, cranial nerve dysfunction resulting in hyposmia or anosmia, unilateral or bilateral facial palsy, impaired hearing, and/or swallowing problems, abnormal outer ears, ossicular malformations, Mondini defect of the cochlea and absent or hypoplastic semicircular canals.

In another embodiment, the medical condition associated with KS is selected from ophthalmic abnormalities, cardiovascular anomalies, hearing loss, kidney abnormalities, skeletal anomalies, dental abnormalities, feeding difficulties, endocrine problems, infection, autoimmune disorders, seizures and developmental difficulties such as autism. Other medical conditions associated with KS include, but are not limited to, developmental delay, cardiovascular malformations, growth deficiency, orofacial clefts, genitourinary anomalies, including cryptorchidism in males, seizures and hearing loss, skeletal anomalies, spinal column abnormalities, including sagittal cleft vertebrae, butterfly vertebrae, narrow intervertebral disc space, and/or scoliosis, hypodontia, susceptibility to infections and autoimmune disorders, gastrointestinal anomalies, including anal atresia; and ophthalmologic anomalies, including ptosis and strabismus.

III. Kits

Another aspect provides a kit for detecting and/or screening for CHARGE syndrome (CS), or an increased likelihood of CS, in a sample, comprising:

(a) at least one detection agent for determining the methylation level of:

-   -   at least 3, optionally at least 5, at least 8, at least 10, at         least 25, at least 44, at least 50, at least 75, at least 100,         at least 125, at least 140, or all CpG loci from (i) Tables 2         and/or 16 and/or (ii) associated CpG loci residing within 300         nucleotides, optionally within 150 nucleotides, of the CpG loci         of (i), and;

(b) instructions for use.

Another aspect provides a kit for detecting and/or screening for CHARGE syndrome (CS), or an increased likelihood of CS, in a sample, comprising:

(a) at least one detection agent for determining the methylation level of:

-   -   at least 2, optionally at least 3, at least 4, at least 6, at         least 8, at least 10, at least 16, at least 20, at least 25, at         least 30, at least 35, at least 40, or all the genes from Tables         2 and/or 16 and;

(b) instructions for use.

Another aspect provides a kit for detecting and/or screening for Kabuki syndrome (KS), or an increased likelihood of KS, in a sample, comprising:

(a) at least one detection agent for determining the methylation level of:

-   -   at least 6, optionally at least 8, at least 10, at least 15, at         least 20, at least 25, at least 46, at least 50, at least 75, at         least 100, at least 125, at least 150, at least 200, at least         250, or all CpG loci from (i) Tables 9 and/or 17 and/or (ii)         associated CpG loci residing within 300 nucleotides, optionally         within 150 nucleotides, of the CpG loci of (i), and;

(b) instructions for use.

Another aspect provides a kit for detecting and/or screening for Kabuki syndrome (KS), or an increased likelihood of KS, in a sample, comprising:

(a) at least one detection agent for determining the methylation level of:

-   -   at least 3, optionally at least 4, at least 6, at least 8, at         least 10, at least 15, at least 20, at least 25, at least 50, at         least 75, at least 100, at least 125, or all the genes from         Tables 9 and/or 17 and;

(b) instructions for use.

In an embodiment, the kit further comprises bisulfite conversion reagents, methylation-dependent restriction enzymes, methylation-sensitive restriction enzymes, PCR reagents, probes and/or primers.

In another embodiment, the kit further comprises a computer-readable medium that causes a computer to compare methylation levels from a sample at the selected genes to one or more control profiles and compute a correlation value between the sample and control profile.

In another embodiment, the kit further comprises a computer-readable medium that causes a computer to compare methylation levels from a sample at the selected CpG loci to one or more control profiles and compute a correlation value between the sample and control profile.

Other features and advantages of the disclosure will become apparent from the following detailed description. It should be understood, however, that the description and the specific examples while indicating preferred embodiments are given by way of illustration only, since various changes and modifications within the spirit and scope of the disclosure will become apparent to those skilled in the art from this description of various embodiments.

EXAMPLES Example 1

DNA methylation was determined in the blood of subjects with CHARGE and a nonsense mutation in CHD7 compared to controls. A set of CpG sites that can be used as a signature to distinguish subjects from controls was identified. This set of CpG sites can be used to distinguish patients from controls and determine if a variant in CHD7 is mostly likely pathogenic or benign. This signature was also specific to those subjects compared to a large sample of population controls. Many of the CpG sites with greater than 10% differences in DNA methylation are known to play a role in early embryonic growth and development. The DNA methylation alterations that occur as a result of heterozygous CHD7 mutations also reveal genes, such as those in the HOXA cluster and FOXP2, which may play a critical role in the aberrant development associated with the clinical spectrum of CHARGE syndrome.

Subjects and Methods Subjects and Clinical Information

Individuals with a clinical diagnosis of CHARGE syndrome, who meet the clinical criteria of Blake²³ or Verloes³⁰, were recruited through the Division of Clinical and Metabolic Genetics at the Hospital for Sick Children in

Toronto. DNA methylation of whole blood was analyzed in 15 DNA samples from individuals with CHD7 pathogenic nonsense mutations. An additional 14 subjects with variants in CHD7 including missense, splice site missense, variants of unknown significance (VUS) in CHD7 that have a clinical diagnosis of CHARGE syndrome and 4 with sequence variants in CHD7 without CHARGE syndrome (Table 1) were compared to 45 age, sex and ethnicity matched controls. Phenotypic information was available for all of the subjects. The control subjects and those with missense mutations in CHD7 were recruited through The Hospital for Sick Children.

All subjects were recruited following informed consent. The study was approved by the Research Ethics Boards of the Hospital for Sick Children Toronto. DNA was extracted from whole blood collected from cases and controls.

Control DNA Methylation Data from Public Databases

Publically available HumanMethylation450 data at the GEO resource DNA methylation data for an additional 1056 control blood samples were downloaded from the GEO public database (http://www.ncbi.nlm.nih.gov/sites/GDSbrowser/).

Methylation Array Analysis

DNA samples were modified using sodium bisulfite (EpiTect PLUS Bisulfite Kit, QIAGEN). The sodium bisulfite converted DNA was then hybridized to the Illumina Infinium HumanMethylation450 BeadChip Array to interrogate over 485577 CpG sites in the human genome. Illumina Genome studio software was used to extract DNA methylation values (β values), calculated after control probe normalization and background subtraction using the formula C/(C+T), and ranging between 0 (no methylation) and 1 (full methylation). Autosomal probes that cross-react with sex chromosome probes, non-specific probes, and probes targeting CpG sites at a known SNP^(31,32) were excluded. The analysis was performed on the remaining 432,601 probes. Since for most CpG sites across the genome DNA methylation is not normally distributed, the non-parametric test to determine changes in DNA methylation between groups was used. For each probe, Mann-Whitney U test was performed to compare 21 blood samples from subjects with a known CHD7 pathogenic mutation samples to 45 controls, followed by the Benjamini-Hochberg correction for multiple testing.

To determine the appropriate significance level for the Mann-Whitney U tests, the volcano plot (FIG. 1) was first examined, which suggested the p-value threshold 0.01. This p-value threshold was confirmed by a series of leave-one-out (LOO) cross-validations on the combined dataset. In each LOO iteration, one sample was removed from the dataset for the subsequent validation step (Tables 3-6). The remaining samples were used to generate median DNA methylation profiles for the subjects containing a CHD7 mutation group and for the control group, respectively. The retained validation sample was then compared to both reference profiles, using only the significant CpGs, and with Pearson correlation as the measure of similarity. The sample was assigned to the group with the more similar profile, and the assignment compared to the true status of the sample (those with a CHD7 nonsense mutation or control). Iterating the LOO process over all 60 samples, the classification accuracy was estimated in terms of the specificity and sensitivity for a given level of significance. To ensure robust results, statistically significant probes were additionally filtered for the effect size. Delta beta (Δβ) was defined for each probe as the difference between average control and average CHD7 nonsense mutation methylation levels (Tables 2 and/or 16). Only those significant probes for which the DNA methylation difference (Δβ) was greater than an absolute value of 0.10 were retained. Statistical analysis was performed in R using custom scripts.

Results CHD7 Signature

The LOO procedure confirmed that the p-value threshold 0.01, when combined with the effect size threshold |Δβ|>0.10, was the necessary significance level at which the LOO procedure makes no classification errors. Applying the statistical tests with these parameters to the full collection of 15 CHD7 nonsense mutation samples and 45 controls, a “signature set” of 146 significant CpG sites was derived. As expected, the set defined a perfect separation between the samples with a pathogenic CHD7 mutation and controls (FIG. 2).

Signature Validation

The resulting set of probes for specific CpG sites were located within the bodies or promoter regions of 44 known genes (Table 2). Several genes had more than one differentially methylated CpG site including FOXP2, HOTAIRM1, SLITRKS and multiple genes in the HOXA cluster. Enrichment analysis of the resulting set using DAVID (http://david.abcc.ncifcrf.gov/) confirmed a statistically significant over-representation in genes related to skeletal, neural and lung development, as well as to transcriptional regulation.

These functional categories are highly relevant to the CHARGE syndrome phenotype, validating the biological importance of the derived DNA methylation signature.

Next the specificity of the signature CpGs on a collection of 1056 normal blood samples derived from GEO was validated. Similar to the LOO procedure, median DNAm profiles for the 15 CHD7 nonsense mutation samples and for the 45 control samples, respectively, were generated. The Pearson correlation of each of the GEO samples with the reference CHD7 profile and the reference control profiles, using the 146 significant CpGs sites was computed. Only 5 samples exhibited a higher correlation with the CHD7 profile, whereas the remaining 1047 samples were classified as normal, resulting in 99.5% specificity (FIG. 3). This high specificity estimate is encouraging, given the diversity and unknown phenotype of the combined data from GEO sources. Similar estimates were tabulated for additional parameter combinations for effect size threshold |Δβ| from 5% to 22% and significance level from p<0.01 to 0.00005 (Tables 3-6).

The signature was then applied to classify 14 subjects with CHD7 mutation that did not result in a nonsense mutation into either pathogenic or benign mutations (FIG. 3). Using the same classification procedure as was used to define the signature, 9 of the variants were predicted to be pathogenic, whereas the remaining samples were predicted to be benign.

Example 2 Summary

To date, approximately two-thirds of Kabuki syndrome patients have an identified mutation in the Lysine (K) Methyltransferase 2D (KMT2D) gene. Mutations in KMT2D may cause downstream alterations in DNA methylation (DNAm), a modification of DNA that can alter gene expression without modifying the DNA sequence itself.

DNA methylation was determined in the blood of subjects with Kabuki syndrome and a nonsense mutation in KMT2D compared to controls and is set of CpG sites that could be used as a signature to distinguish subjects from controls were identified. This set of CpG sites is used to distinguish patients from controls and determine if a variant in KMT2D is pathogenic or benign. This signature is also specific to those subjects compared to a large sample of population controls. Many of the CpG sites with greater than 15% differences in DNA methylation are known to play a role in early embryonic growth and development. The DNA methylation alterations that occur as a result of heterozygous KMT2D mutations also reveal genes, such as those in the HOXA cluster, laminin beta 2 (LAMB2) and myosin F1 (MYOF1), which may play a critical role in the aberrant development associated with the clinical spectrum of Kabuki syndrome.

Subjects and Methods Subjects and Clinical Information

Individuals with a clinical diagnosis of Kabuki syndrome³⁶ were recruited through the Division of Clinical and Metabolic Genetics at the Hospital for Sick Children in Toronto, or the Center for Human Genetics, Inc., Cambridge, USA. DNA methylation of whole blood was analyzed in 11 DNA samples from individuals with KMT2D pathogenic nonsense mutations. An additional 9 subjects with variants in KMT2D including 1 missense mutation, 1 variant of unknown significance (VUS) in KMT2D that has a clinical diagnosis of Kabuki syndrome and 6 with missense variants in KMT2D without Kabuki syndrome (Table 7) compared to 45 age, sex and ethnicity matched controls. There was also one additional subject that had a diagnosis of Kabuki syndrome but the mutation status was not known at the time of analysis. The control subjects and those with missense mutations in KMT2D were recruited through The Hospital for Sick Children and Simons Simplex Collection³⁷.

All subjects were recruited following informed consent. DNA was extracted from whole blood collected from cases and controls.

Control DNA Methylation Data from Public Databases

Publically available HumanMethylation450 data at the GEO resource DNA methylation data for an additional 1056 control blood samples were downloaded from the GEO public database (http://www.ncbi.nlm.nih.gov/sites/GDSbrowser/).

Methylation Array Analysis

DNA samples were modified using sodium bisulfite (EpiTect PLUS Bisulfite Kit, QIAGEN). The sodium bisulfite converted DNA was then hybridized to the Illumina Infinium HumanMethylation450 BeadChip Array to interrogate over 485,577 CpG sites in the human genome. Illumina Genome studio software was used to extract DNA methylation values (β values), calculated after control probe normalization and background subtraction using the formula C/(C+T), and ranging between 0 (no methylation) and 1 (full methylation). Autosomal probes that cross-react with sex chromosome probes, non-specific probes, and probes targeting CpG sites at a known SNP^(38,39) were excluded. The analysis was performed on the remaining 422, 139 probes. Since for most CpG sites across the genome DNA methylation is not normally distributed, the non-parametric test was used to determine changes in DNA methylation between groups. For each probe, Mann-Whitney

U test was performed to compare 11 blood samples from subject with a known KMT2D pathogenic mutation samples and 45 controls, followed by the Benjamini-Hochberg correction for multiple testing.

To determine the appropriate significance level for the Mann-Whitney U tests, the volcano plot (FIG. 4) was first examined, which suggested that the p-value threshold 0.05. This p-value threshold was confirmed by a series of leave-one-out (LOO) cross-validations on the combined dataset. In each LOO iteration one sample was removed from the dataset for the subsequent validation step (Table 8). The remaining samples were used to generate median DNA methylation profiles for the subjects containing a KMT2D mutation group and for the control group, respectively. The retained validation sample was then compared to both reference profiles, using only the significant CpGs, and with Pearson correlation as the measure of similarity. The sample was assigned to the group with the more similar profile, and the assignment compared to the true status of the sample (those with a KMT2D nonsense mutations or control). Iterating the LOO process over all 56 samples, the classification accuracy was estimated in terms of the specificity and sensitivity for a given level of significance. To ensure robust results, statistically significant probes were additionally filtered for the effect size. Delta beta (Δβ) was defined for each probe as the difference between average control and average KMT2D nonsense mutation methylation levels (Table 3). Only those significant probes for which the DNA methylation difference (Δβ) was greater than an absolute value of 15% were retained. Statistical analysis was performed in R using custom scripts.

Results KMT2D Signature

The LOO procedure confirmed that the p-value threshold 0.05, when combined with the effect size threshold |Δβ|>15%, was the necessary significance level at which the LOO procedure makes no classification errors (see Table 8). Applying the statistical tests with these parameters to the full collection of 11 KMT2D nonsense mutation samples and 45 controls, a “signature set” of 287 significant CpG sites was derived. As expected, the set defined a perfect separation between the samples with a pathogenic KMT2D mutation and controls (FIG. 5).

The resulting set of probes for specific CpG sites were located within the bodies or promoter regions of 162 known genes (Table 9). Several genes had more than one differentially methylated CpG site including LAMB2, MYO1F, AGAP2 ArfGAP with GTPase domain, ankyrin repeat and PH domain 2 and multiple genes in the HOXA cluster, with the most probes differentially methylated in HOXA4. An additional 28 genes (Table 17) have been identified that include a muscle specific isoform CPT1B, which had more than one differentially methylated CpG site.

Next, the specificity of the signature CpGs on a collection of 1056 normal blood samples derived from GEO was validated. Similar to the LOO procedure, median DNAm profiles for the 11 KMT2D nonsense mutation samples and for the 45 control samples, respectively, were generated. The Pearson correlation of each of the GEO samples with the reference KMT2D profile and the reference control profiles, using the 287 significant CpGs sites. None of these samples exhibited a higher correlation with the KMT2D profile therefore there was a 100% specificity (FIG. 5). This high specificity estimate is encouraging, given the diversity and unknown phenotype of the combined data from GEO sources. Similar estimates were tabulated for additional parameter combinations for effect size threshold |Δβ| from 5% to 25% and significance level from p<0.01 to 0.00001 (Tables 10-15).

The signature was then applied to classify 9 subjects with KMT2D mutation that did not result in a nonsense mutation into either pathogenic or benign mutations (FIG. 6). Using the same classification procedure as was used to define the signature, 1 of the variants was predicted to be pathogenic, whereas the remaining samples were predicted to be benign, including the subject for which molecular testing is still pending (Table 8). There was a high correlation between the clinical phenotype and the corresponding KMT2D-specific DNA methylation profile.

While the present disclosure has been described with reference to what are presently considered to be the examples, it is to be understood that the disclosure is not limited to the disclosed examples. To the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

All publications, patents and patent applications are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety.

TABLE 1 CHD7 mutation information for all cases Mutation Sample ID Sex Nucleotide Protein Type CHD7-16 F c.7282C > T p.Arg2428X nonsense 77458 M c.3526C > T p.Gln1176X nonsense CHD7-2 F c.934C > T p.Arg312X nonsense 147372 M c562C > T p.Gly188X nonsense CHD7-66C M c.1327delATGGG p.Met443Asnfs*130 deletion CHD7-12 M c.2504_2508delATCTT p.Tyr835Serfs*14 deletion 11D/0324 M c.1990G > T p.Glu664X nonsense 68779 F c.3377dupT p.Leu1126fs*46 duplication CHD7-4 M c.2585delA p.Lys862Serfs*26 deletion 177040 F c.2905_2906del p.Arg969Glyfs*25 deletion SP-CHD7 M c.7636G > T p.Glu2546X nonsense CHD7-8 M c.361delC p.Gly121Valfs*90 deletion CHD7-11 M c.2504_2508delATCTT p.Tyr835Serfs*14 deletion 11D/0323 M c.7717-7720del p.Glu2537X nonsense DL101555 M c.5458C > T p.Arg1820X nonsense

TABLE 2 146 CpG loci corresponding to 44 genes were identified as showing a statistically significant (corrected p-value <0.01) difference in CS and non-CS controls. Benjamini- Hochberg DNA corrected p- Absolute methylation Mean not- Mean Illumine ID p-value value deltaBeta deltaBeta effect CHARGE CHARGE Gene Symbol cg17569124 2.63E−13 2.84638E−08 0.24269876 0.24269876 GAIN 0.628406184 0.87110494 HOXA5; HOXA- AS3 cg25307665 7.14E−13 4.41479E−08 0.2380737 0.2380737 GAIN 0.659500102 0.8975738 HOXA5; HOXA- AS3 cg12128839 3.65E−12 7.88853E−08 0.22084216 0.22084216 GAIN 0.62802854 0.8488707 HOXA5; HOXA5; HOXA- AS3 cg05076221  1.4E−11 1.95705E−07 0.21817837 0.21817837 GAIN 0.571198187 0.78937656 HOXA5; HOXA- AS3 cg19759481 1.69E−12 6.00554E−08 0.19441479 0.19441479 GAIN 0.68921206 0.883626853 HOXA5; HOXA5; HOXA- AS3 cg04863892  1.5E−13 2.84638E−08 0.1890898 0.1890898 GAIN 0.686186942 0.875276747 HOXA5; HOXA5; HOXA- AS3 cg04053108  6.9E−09 3.31436E−05 0.18874139 0.18874139 GAIN 0.248886338 0.437627727 VWF cg02005600 2.52E−12 6.41032E−08 0.17852388 0.17852388 GAIN 0.705460591 0.883984473 HOXA5; HOXA- AS3 cg23936031  1.8E−12 6.00554E−08 0.17634521 0.17634521 GAIN 0.765829049 0.942174257 HOXA5; HOXA5; HOXA- AS3 cg09319828 1.24E−05 0.008204599 0.17363223 0.17363223 GAIN 0.324044113 0.497676347 TTC24 cg02916332 1.13E−12 5.42167E−08 0.17187598 0.17187598 GAIN 0.651970609 0.823846587 HOXA5; HOXA- AS3 cg03368099  6.9E−09 3.31436E−05 0.16690336 0.16690336 GAIN 0.564462407 0.731365767 HOXA5; HOXA- AS3 cg11724970 5.23E−12 9.42015E−08 0.16262001 0.16262001 GAIN 0.74328516 0.905905167 HOXA5; HOXA- AS3 cg18274664 3.76E−14  1.6265E−08 0.15866987 0.15866987 GAIN 0.595106733 0.753776607 APP; APP; APP; APP cg03529432  1.3E−10 1.25096E−06 0.15645583 0.15645583 GAIN 0.104669809 0.26112564 HOXA6; HOXA6; HOXA- AS3; HOXA- AS3 cg05835726 3.02E−12 7.26996E−08 0.15449369 0.15449369 GAIN 0.74016994 0.894663627 HOXA5; HOXA- AS3 cg02248486 1.69E−12 6.00554E−08 0.15406701 0.15406701 GAIN 0.725477107 0.87954412 HOXA5; HOXA5; HOXA- AS3 cg17432857 3.65E−12 7.88853E−08 0.15398978 0.15398978 GAIN 0.650043193 0.804032973 HOXA5; HOXA- AS3 cg14882265 2.57E−11 3.27214E−07 0.15191829 0.15191829 GAIN 0.734545273 0.886463567 HOXA5; HOXA- AS3 cg11321156 7.14E−13 4.41479E−08 0.14905949 0.14905949 GAIN 0.598582929 0.74764242 APP; APP; APP; APP; APP; APP; APP; AP cg01370449 7.14E−13 4.41479E−08 0.14892232 0.14892232 GAIN 0.72699072 0.87591304 HOXA5; HOXA5; HOXA- AS3 cg20517050  1.4E−11 1.95705E−07 0.14656577 0.14656577 GAIN 0.73752756 0.884093327 HOXA5; HOXA- AS3 cg14044640 1.01E−10 1.09301E−06 0.14640823 0.14640823 GAIN 0.043525592 0.189933825 HOXA6; HOXA6; HOXA- AS3; HOXA- AS3 cg23269692 5.23E−12 9.42015E−08 0.14592414 0.14592414 GAIN 0.631808842 0.777732987 APP; APP; APP; APP; APP; APP; APP; AP cg23129930 1.16E−08 4.88234E−05 0.14555376 0.14555376 GAIN 0.599694249 0.745248013 HOXA6; HOXA- AS3; HOXA-AS3 cg26023912 4.55E−11  5.6184E−07 0.1445049 0.1445049 GAIN 0.694275447 0.838780347 HOXA5; HOXA- AS3 cg25866143 7.33E−12 1.21988E−07 0.13778995 0.13778995 GAIN 0.751083682 0.888873633 HOXA5; HOXA5; HOXA- AS3 cg06237983  6.9E−09 3.31436E−05 0.13205043 0.13205043 GAIN 0.333813007 0.46586344 HOXA6; HOXA6; HOXA- AS3; HOXA- AS3 cg02646423 1.02E−11 1.58003E−07 0.13102906 0.13102906 GAIN 0.666693496 0.79772256 HOXA5; HOXA- AS3 cg17994139 4.25E−10 3.28466E−06 0.12790603 0.12790603 GAIN 0.038604284 0.166510311 HOXA6; HOXA6; HOXA- AS3; HOXA- AS3 cg14014955 1.02E−11 1.58003E−07 0.12376528 0.12376528 GAIN 0.758782409 0.882547693 HOXA5; HOXA- AS3 cg24168308 1.13E−12 5.42167E−08 0.12357484 0.12357484 GAIN 0.616836109 0.740410953 APP; APP; APP; APP; APP; APP; APP; AP cg00048370 8.98E−08 0.000268032 0.11921982 0.11921982 GAIN 0.598960227 0.718180047 cg00969405 1.69E−12 6.00554E−08 0.11655506 0.11655506 GAIN 0.756021891 0.872576947 HOXA5; HOXA- AS3 cg23054456 1.63E−08 6.64247E−05 0.11540171 0.11540171 GAIN 0.56865384 0.684055553 cg23204968 2.52E−12 6.41032E−08 0.11131566 0.11131566 GAIN 0.808302327 0.919617987 HOXA5; HOXA- AS3 cg08319974 4.07E−07 0.000786218 0.11088457 0.11088457 GAIN 0.525400382 0.636284953 cg22469274 5.31E−10 3.82878E−06 0.1106167 0.1106167 GAIN 0.044194163 0.154810859 HOXA6; HOXA6; HOXA- AS3; HOXA- AS3 cg15571561 3.57E−07 0.000725693 0.10922618 0.10922618 GAIN 0.188979984 0.298206167 ARPP21; ARPP21; cg16923485 2.74E−09 1.56011E−05 0.1078056 0.1078056 GAIN 0.460578478 0.568384073 SLCO1A2; SLCO1A2 cg19816811 6.01E−06 0.005092068 0.10705528 0.10705528 GAIN 0.519283504 0.626338787 HOXA6; HOXA- AS3; HOXA-AS3 cg27151303 2.24E−06 0.002593592 0.10702693 0.10702693 GAIN 0.52929966 0.636326587 HOXA-AS3 cg24378559 4.81E−09 2.50546E−05 0.1052376 0.1052376 GAIN 0.442865982 0.54810358 cg20817131 2.52E−12 6.41032E−08 0.10463113 0.10463113 GAIN 0.7788633 0.883494433 HOXA5; HOXA- AS3 cg25267863 1.04E−07 0.000297464 0.10446312 0.10446312 GAIN 0.292180311 0.396643427 cg05928186 2.56E−08 0.000102521 0.10372241 0.10372241 GAIN 0.44578503 0.54950744 HOXA6; HOXA- AS3; HOXA-AS3 cg14658493 5.23E−12 9.42015E−08 0.10369845 0.10369845 GAIN 0.814620413 0.918318867 HOXA5; HOXA- AS3 cg06388363  1.4E−06 0.001875868 0.10218302 0.10218302 GAIN 0.417598078 0.5197811 cg15297220 5.76E−08 0.000193245 0.10154844 0.10154844 GAIN 0.360729758 0.462278193 cg20974609 2.57E−11 3.27214E−07 0.10138775 0.10138775 GAIN 0.843193793 0.94458154 HOXA5; HOXA- AS3 cg07070348 8.68E−07 0.001345676 0.10103344 0.10103344 GAIN 0.46125528 0.56228872 cg25174844 1.58E−06 0.002040974 0.10081944 0.10081944 GAIN 0.501924336 0.60274378 cg11096515 1.38E−07 0.00036862  −0.10008737 0.10008737 LOSS 0.489812578 0.389725207 COL4A2 cg00026909 4.07E−07 0.000786218 −0.10059218 0.10059218 LOSS 0.346087567 0.245495387 DAB1 cg20292791 1.11E−06 0.001570483 −0.10069147 0.10069147 LOSS 0.805879264 0.705187793 DAB1 cg23772122 2.74E−07 0.000596022 −0.10145315 0.10145315 LOSS 0.61480136 0.513348213 ANO3 cg24796998 6.69E−08 0.000217703 −0.10167643 0.10167643 LOSS 0.499954038 0.398277607 cg24750308  1.2E−07 0.000334393 −0.1019464 0.1019464 LOSS 0.487743396 0.385796993 NOX4; NOX4; NOX4; NOX4 cg21758126 1.12E−05 0.007790401 −0.1025113 0.1025113 LOSS 0.538915104 0.4364038 NR4A2 cg07769947 8.98E−08 0.000268032 −0.10260998 0.10260998 LOSS 0.546605162 0.443995187 cg20955836 8.23E−06 0.006348011 −0.10270363 0.10270363 LOSS 0.339719751 0.23701612 BMP7 cg14897238 6.01E−06 0.005092068 −0.10271335 0.10271335 LOSS 0.434702618 0.331989267 cg01450725 4.25E−08 0.000155777 −0.10307694 0.10307694 LOSS 0.340429131 0.237352193 cg09113483 9.12E−06 0.006794034 −0.10330673 0.10330673 LOSS 0.664091156 0.560784427 cg22011526 1.37E−05 0.008679024 −0.10340502 0.10340502 LOSS 0.747030156 0.643625133 C6orf89; C6orf89; C6orf89; C6orf89 cg23900293 1.11E−06 0.001570483 −0.10436904 0.10436904 LOSS 0.528886009 0.424516973 cg09741912 8.23E−09 3.82658E−05 −0.10475007 0.10475007 LOSS 0.768298233 0.663548167 cg11598935 9.81E−07 0.001437937 −0.10498738 0.10498738 LOSS 0.516474982 0.411487607 BMP7 cg11704490 7.42E−06 0.005879569 −0.10499976 0.10499976 LOSS 0.707848818 0.602849053 cg19655952 1.24E−09 8.13644E−06 −0.10503044 0.10503044 LOSS 0.652592453 0.547562013 FOXP2; FOXP2; FOXP2; FOXP2; FOXP cg15801019  2.8E−06 0.003048286 −0.10518774 0.10518774 LOSS 0.511141729 0.405953987 LAMA2; LAMA2 cg20811236 1.12E−05 0.007790401 −0.10538143 0.10538143 LOSS 0.52218622 0.416804793 cg16968885 8.68E−07 0.001345676 −0.10543721 0.10543721 LOSS 0.785359396 0.679922187 COL11A1; COL11A1; COL11A1; COL1 cg20592075  1.2E−07 0.000334393 −0.10571857 0.10571857 LOSS 0.644023313 0.538304747 cg19743254 1.01E−05 0.007274077 −0.10610715 0.10610715 LOSS 0.625013382 0.518906233 OPCML; OPCML cg27536286 6.78E−07 0.001158528 −0.10668088 0.10668088 LOSS 0.651200002 0.544519127 cg25436634 6.78E−07 0.001158528 −0.10737378 0.10737378 LOSS 0.523050418 0.415676633 SOX2-OT; SOX2- OT; SOX2-OT cg18951332 1.67E−10 1.5341E−06 −0.10767814 0.10767814 LOSS 0.738900909 0.631222767 cg24526899 1.37E−05 0.008679024 −0.10776459 0.10776459 LOSS 0.669045324 0.561280733 BMP4; BMP4 cg22321572 1.25E−06 0.001702533 −0.10825609 0.10825609 LOSS 0.317658142 0.209402057 MLLT4-AS1 cg10228555 5.41E−06 0.004745665 −0.10858265 0.10858265 LOSS 0.453158811 0.34457616 LOC100128770 cg25008182 3.32E−09 1.77048E−05 −0.10958067 0.10958067 LOSS 0.834894593 0.725313927 cg20263045 2.24E−06 0.002593592 −0.10962959 0.10962959 LOSS 0.767880947 0.658251353 HHIP cg06602723 1.52E−09 9.53506E−06 −0.10984647 0.10984647 LOSS 0.377775487 0.26792902 HOXB8 cg25701444 5.26E−07 0.000952952 −0.10987376 0.10987376 LOSS 0.733034304 0.62316054 LOC400043 cg10886095 4.36E−06 0.004146153 −0.11155878 0.11155878 LOSS 0.557715016 0.446156233 CCDC60 cg13749822 1.66E−05 0.009675336 −0.11160844 0.11160844 LOSS 0.300161093 0.188552656 HHIP; HHIP-AS1 cg17654050 1.58E−06 0.002040974 −0.11169961 0.11169961 LOSS 0.54445336 0.432753753 NR4A2 cg26673377 3.57E−07 0.000725693 −0.11233179 0.11233179 LOSS 0.683894816 0.571563027 cg08657492 1.51E−05 0.00917751  −0.11328691 0.11328691 LOSS 0.51272948 0.399442573 HOXA4 cg20706134 2.09E−07 0.000494656 −0.11378449 0.11378449 LOSS 0.722188707 0.60840422 PCDH20 cg05232889 1.16E−08 4.88234E−05 −0.11419818 0.11419818 LOSS 0.758496844 0.64429866 FOXP2; FOXP2; FOXP2; FOXP2; FOXP cg07659054 8.23E−09 3.82658E−05 −0.11443317 0.11443317 LOSS 0.361695007 0.24726184 HOXA1; HOXA1; HOTAIRM1; HOTAIRM1 cg12806882 4.25E−08 0.000155777 −0.11453851 0.11453851 LOSS 0.566396433 0.45185792 FMN2 cg18871253 2.74E−09 1.56011E−05 −0.11623746 0.11623746 LOSS 0.736403029 0.620165567 FOXP2; FOXP2; FOXP2; FOXP2; FOXP cg25942940  1.4E−11 1.95705E−07 −0.1171622 0.1171622 LOSS 0.781304316 0.664142113 cg13320964 9.81E−07 0.001437937 −0.11776404 0.11776404 LOSS 0.661345138 0.543581093 cg17461600 2.74E−07 0.000596022 −0.11792734 0.11792734 LOSS 0.691368147 0.573440807 DAB1 cg15648345 3.13E−07 0.000654544 −0.1181466 0.1181466 LOSS 0.613033044 0.494886447 MKS1; MKS1 cg18546840 1.86E−09 1.14743E−05 −0.11855055 0.11855055 LOSS 0.790301131 0.67175058 FOXP2; FOXP2; FOXP2; FOXP2; FOXP cg09203312  1.4E−06 0.001875868 −0.12039032 0.12039032 LOSS 0.671637149 0.551246833 GJB6; GJB6; GJB6; GJB6; GJB6 cg02211646  1.3E−10 1.25096E−06 −0.12069127 0.12069127 LOSS 0.779895987 0.65920472 FOXP2; FOXP2; FOXP2; FOXP2; FOXP cg18805066 9.79E−09 4.36653E−05 −0.12069718 0.12069718 LOSS 0.287220951 0.166523775 HOXA1; HOXA1; HOTAIRM1; HOTAIRM1 cg00428457 5.41E−06 0.004745665 −0.12072828 0.12072828 LOSS 0.769902418 0.64917414 cg01746241 1.11E−06 0.001570483 −0.12217225 0.12217225 LOSS 0.676761909 0.55458966 KIAA1161 cg24549912 5.26E−07 0.000952952 −0.12223264 0.12223264 LOSS 0.336860567 0.214627927 cg11857140 3.32E−09 1.77048E−05 −0.1236919 0.1236919 LOSS 0.735227656 0.61153576 KIRREL3; KIRREL3 cg24786986 5.31E−10 3.82878E−06 −0.12397933 0.12397933 LOSS 0.7449648 0.620985473 FOXP2; FOXP2; FOXP2; FOXP2; FOXP cg08959039 5.41E−06 0.004745665 −0.12404245 0.12404245 LOSS 0.472527078 0.348484627 COL4A2 cg22154659 2.69E−10 2.32622E−06 −0.12878474 0.12878474 LOSS 0.537517758 0.408733013 HOXA1; HOXA1; HOTAIRM1; HOTAIRM1 cg09517766 9.81E−07 0.001437937 −0.12995329 0.12995329 LOSS 0.6572662 0.527312913 cg15161959 9.79E−09 4.36653E−05 −0.13054628 0.13054628 LOSS 0.477714369 0.347168087 cg11758841 7.79E−11 8.84149E−07 −0.13651604 0.13651604 LOSS 0.709946164 0.573430127 PARVA cg25598685 1.16E−08 4.88234E−05 −0.13769051 0.13769051 LOSS 0.73225986 0.594569353 cg25556579 1.01E−09 6.93405E−06 −0.13814846 0.13814846 LOSS 0.509734767 0.371586307 TBX5; TBX5; TBX5 cg25037165 6.61E−10 4.68485E−06 −0.13905994 0.13905994 LOSS 0.884593136 0.745533193 TEAD1 cg06218338 3.91E−06 0.003844825 −0.13951377 0.13951377 LOSS 0.290344878 0.150831105 cg26264232 1.16E−08 4.88234E−05 −0.13993212 0.13993212 LOSS 0.272384147 0.132452026 HOTAIRM1; HOTAIRM1 cg19981409 9.79E−09 4.36653E−05 −0.14255474 0.14255474 LOSS 0.442544042 0.299989307 NOX4; NOX4; NOX4; NOX4 cg23111488 1.01E−09 6.93405E−06 −0.1498176 0.1498176 LOSS 0.759792356 0.609974753 cg00525681 3.91E−06 0.003844825 −0.14995196 0.14995196 LOSS 0.615382438 0.465430473 SLITRK5 cg06911613 4.95E−08 0.000174192 −0.15045288 0.15045288 LOSS 0.649693296 0.49924042 cg06906435 1.24E−09 8.13644E−06 −0.15057591 0.15057591 LOSS 0.527015647 0.376439733 C14orf177 cg17376609 1.59E−07 0.000404111 −0.15256603 0.15256603 LOSS 0.752634513 0.60006848 SLITRK5 cg13746854 2.37E−06 0.002735892 −0.15289842 0.15289842 LOSS 0.53473938 0.38184096 KIAA1161 cg12115302 1.38E−07 0.00036862  −0.15317369 0.15317369 LOSS 0.490363404 0.337189717 cg08657654 1.59E−07 0.000404111 −0.1542548 0.1542548 LOSS 0.769303353 0.615048553 HOTAIRM1; HOTAIRM1 cg16370398 1.66E−06 0.002126946 −0.15506739 0.15506739 LOSS 0.511144073 0.35607668 HOXC4; HOXC4 cg16787483 4.07E−07 0.000786218 −0.16228425 0.16228425 LOSS 0.717952758 0.555668507 SLITRK5 cg21090457 3.39E−10 2.76566E−06 −0.16579188 0.16579188 LOSS 0.617719138 0.451927253 ROBO2; ROBO2 cg16915863 3.14E−06 0.003366444 −0.16787453 0.16787453 LOSS 0.773898147 0.606023613 LOC400043 cg08941355 5.31E−10 3.82878E−06 −0.17084311 0.17084311 LOSS 0.672878658 0.502035547 HOXA1; HOXA1 cg03906434 4.36E−06 0.004146153 −0.17673643 0.17673643 LOSS 0.316082895 0.13934647 cg09823859 5.76E−08 0.000193245 −0.17889435 0.17889435 LOSS 0.654204142 0.475309793 SLITRK5 cg05757365 4.07E−07 0.000786218 −0.17921325 0.17921325 LOSS 0.614664191 0.43545094 SLITRK5 cg04707013 6.01E−06 0.005092068 −0.18328041 0.18328041 LOSS 0.707909664 0.52462925 cg23865240 7.33E−12 1.21988E−07 −0.18696432 0.18696432 LOSS 0.505623411 0.318659087 HOXA1; HOXA1 cg18751141 7.97E−11 8.84149E−07 −0.1901929 0.1901929 LOSS 0.497364882 0.30717198 HOTAIRM1; HOTAIRM1 cg24626752 8.68E−07 0.001345676 −0.19303999 0.19303999 LOSS 0.676344478 0.483304487 SLITRK5 cg17881200 2.69E−10 2.32622E−06 −0.19311652 0.19311652 LOSS 0.507025549 0.313909033 HOTAIRM1; HOTAIRM1 cg26168643 1.78E−06 0.002207935 −0.19328866 0.19328866 LOSS 0.608304222 0.41501556 SLITRK5 cg17485838 3.39E−10 2.76566E−06 −0.19332356 0.19332356 LOSS 0.540119302 0.34679574 HOTAIRM1; HOTAIRM1 cg02611934 1.82E−07 0.000448384 −0.2035436 0.2035436 LOSS 0.608860269 0.405316673 SLITRK5 cg07278425 5.23E−12 9.42015E−08 −0.21052294 0.21052294 LOSS 0.616841918 0.40631898 HOTAIRM1; HOTAIRM1 cg07318204 1.38E−07 0.00036862  −0.21626875 0.21626875 LOSS 0.744438569 0.52816982 HHIP; HHIP-AS1 cg00106345 5.98E−11 6.98956E−07 −0.219793 0.219793 LOSS 0.455452644 0.235659647 HOTAIRM1; HOTAIRM1 Genomic Relation to Coordinate transcription Illumine ID Genome_Build Chromosome (NCBI, hg19) Strand Relation_to_UCSC_CpG_Island start site (TSS) cg17569124 37 7 27183643 − Island HOXA- AS3(body); HOXA5 (tss1500) cg25307665 37 7 27183694 − Island HOXA- AS3(body); HOXA5 (tss1500) cg12128839 37 7 27183436 − Island HOXA- AS3(body); HOXA5 (tss200) cg05076221 37 7 27182637 + Island HOXA- AS3(body); HOXA5 (body) cg19759481 37 7 27183401 − Island HOXA- AS3(body); HOXA5 (tss200) cg04863892 37 7 27183375 − Island HOXA- AS3(body); HOXA5 (tss200) cg04053108 37 12 6166028 − Island VWF(body) cg02005600 37 7 27183686 − Island HOXA- AS3(body); HOXA5 (tss1500) cg23936031 37 7 27183133 + Island HOXA- AS3(body); HOXA5 (body) cg09319828 37 1 156551787 − TTC24(body) cg02916332 37 7 27183591 + Island HOXA- AS3(body); HOXA5 (tss1500) cg03368099 37 7 27184521 − Island HOXA- AS3(body); HOXA5 (tss1500) cg11724970 37 7 27182493 − N_Shore HOXA- AS3(body); HOXA5 (body) cg18274664 37 21 27372461 − APP(body) cg03529432 37 7 27187502 − Island HOXA- AS3(body); HOXA6 (tss200) cg05835726 37 7 27183861 − Island HOXA- AS3(body); HOXA5 (tss1500) cg02248486 37 7 27183196 − Island HOXA- AS3(body); HOXA5 (body) cg17432857 37 7 27184438 − Island HOXA- AS3(body); HOXA5 (tss1500) cg14882265 37 7 27184375 + Island HOXA- AS3(body); HOXA5 (tss1500) cg11321156 37 21 27372396 − APP(body) cg01370449 37 7 27183369 + Island HOXA- AS3(body); HOXA5 (tss200) cg20517050 37 7 27183806 − Island HOXA- AS3(body); HOXA5 (tss1500) cg14044640 37 7 27187560 + Island HOXA- AS3(body); HOXA6 (tss200) cg23269692 37 21 27372446 + APP(body) cg23129930 37 7 27186993 + Island HOXA- AS3(body); HOXA6 (body) cg26023912 37 7 27184369 + Island HOXA- AS3(body); HOXA5 (tss1500) cg25866143 37 7 27183262 + Island HOXA- AS3(body); HOXA5 (body) cg06237983 37 7 27187269 + Island HOXA- AS3(body); HOXA6 (body) cg02646423 37 7 27183794 − Island HOXA- AS3(body); HOXA5 (tss1500) cg17994139 37 7 27187556 + Island HOXA- AS3(body); HOXA6 (tss200) cg14014955 37 7 27183701 + Island HOXA- AS3(body); HOXA5 (tss1500) cg24168308 37 21 27372387 − APP(body) cg00048370 37 6 164506939 − cg00969405 37 7 27184441 − Island HOXA- AS3(body); HOXA5 (tss1500) cg23054456 37 6 164506981 − cg23204968 37 7 27183816 − Island HOXA- AS3(body); HOXA5 (tss1500) cg08319974 37 6 164506861 + cg22469274 37 7 27187553 + Island HOXA- AS3(body); HOXA6 (tss200) cg15571561 37 3 35706161 − ARPP21(body) cg16923485 37 12 21476904 + SLCO1A2(body) cg19816811 37 7 27188364 + N_Shore HOXA- AS3(body); HOXA6 (tss1500) cg27151303 37 7 27184821 − Island HOXA- AS3(body) cg24378559 37 7 156889254 + cg20817131 37 7 27184167 − Island HOXA- AS3(body); HOXA5 (tss1500) cg25267863 37 7 1363124 − Island cg05928186 37 7 27187102 + Island HOXA- AS3(body); HOXA6 (body) cg14658493 37 7 27184077 − Island HOXA- AS3(body); HOXA5 (tss1500) cg06388363 37 6 164507305 − cg15297220 37 4 134589655 + cg20974609 37 7 27181671 − N_Shore HOXA- AS3(body); HOXA5 (body) cg07070348 37 12 130555007 + cg25174844 37 15 73195113 + cg11096515 37 13 111062287 + COL4A2(body) cg00026909 37 1 58089001 + DAB1(body) cg20292791 37 1 58089357 + DAB1(body) cg23772122 37 11 26355628 + S_Shore ANO3(body) cg24796998 37 17 70383845 + cg24750308 37 11 89225014 − S_Shore NOX4(body); NOX4 (tss1500) cg21758126 37 2 157183291 − N_Shore NR4A2(body) cg07769947 37 2 220601262 − cg20955836 37 20 55836224 + N_Shelf BMP7(body) cg14897238 37 21 43198283 − N_Shore cg01450725 37 4 154714852 + S_Shore cg09113483 37 1 61517807 − N_Shore cg22011526 37 6 36857605 + S_Shelf C6orf89(body) cg23900293 37 11 115924505 − cg09741912 37 11 114921894 − cg11598935 37 20 55837619 + N_Shore BMP7(body) cg11704490 37 2 162284894 − S_Shore cg19655952 37 7 114055204 + FOXP2(body) cg15801019 37 6 129203783 − LAMA2(tss1500) cg20811236 37 18 46501400 + N_Shore cg16968885 37 1 103574619 − COL11A1(tss1500) cg20592075 37 7 45921668 − cg19743254 37 11 132735814 + OPCML(body) cg27536286 37 13 27414220 + cg25436634 37 3 181045270 + SOX2-OT(body) cg18951332 37 2 220777552 − cg24526899 37 14 54424149 + S_Shore BMP4(tss1500) cg22321572 37 6 168225923 − N_Shore MLLT4- AS1(body) cg10228555 37 16 3088480 + S_Shore LOC100128770 (body) cg25008182 37 3 182123703 + cg20263045 37 4 145655974 − HHIP(body) cg06602723 37 17 46693336 − N_Shore HOXB8(tss1500) cg25701444 37 12 54521977 − S_Shore LOC400043(body) cg10886095 37 12 119935697 − CCDC60(body) cg13749822 37 4 145566663 − Island HHIP(tss1500); HHIP- AS1(body) cg17654050 37 2 157184978 + N_Shore NR4A2(body) cg26673377 37 6 123182996 + cg08657492 37 7 27170832 + S_Shore HOXA4(tss1500) cg20706134 37 13 61990025 + PCDH20(tss1500) cg05232889 37 7 114055419 + FOXP2(body) cg07659054 37 7 27134225 − Island HOTAIRM1(tss1500); HOXA1(body) cg12806882 37 1 240572391 − S_Shelf FMN2(body) cg18871253 37 7 114055137 + FOXP2(body) cg25942940 37 1 8270645 − N_Shore cg13320964 37 4 138114823 + cg17461600 37 1 57983368 − DAB1(body) cg15648345 37 17 56297360 + S_Shore MKS1(tss1500) cg18546840 37 7 114055123 + FOXP2(body) cg09203312 37 13 20805196 + N_Shore GJB6(body) cg02211646 37 7 114055210 + FOXP2(body) cg18805066 37 7 27134259 − Island HOTAIRM1(tss1500); HOXA1(body) cg00428457 37 2 119887680 − cg01746241 37 9 34370835 − Island KIAA1161(body) cg24549912 37 5 50692281 + N_Shelf cg11857140 37 11 126372533 + KIRREL3(body) cg24786986 37 7 114055133 + FOXP2(body) cg08959039 37 13 111062266 + COL4A2(body) cg22154659 37 7 27134369 − N_Shore HOTAIRM1(tss1500); HOXA1(body) cg09517766 37 10 44894102 − cg15161959 37 2 177020616 − N_Shelf cg11758841 37 11 12530155 + PARVA(body) cg25598685 37 11 42617544 + cg25556579 37 12 114829194 + TBX5(body) cg25037165 37 11 12824283 − TEAD1(body) cg06218338 37 7 27231894 − Island cg26264232 37 7 27138751 − S_Shelf HOTAIRM1(body) cg19981409 37 11 89225042 − S_Shore NOX4(body); NOX4 (tss1500) cg23111488 37 5 144538350 − cg00525681 37 13 88329151 − N_Shore SLITRK5(body) cg06911613 37 16 85846184 − S_Shore cg06906435 37 14 99177777 − C14orf177(tss200) cg17376609 37 13 88328813 + N_Shore SLITRK5(body) cg13746854 37 9 34370894 − Island KIAA1161(body) cg12115302 37 12 30323676 + S_Shore cg08657654 37 7 27138974 + S_Shelf HOTAIRM1(body) cg16370398 37 12 54448913 + S_Shore HOXC4(body) cg16787483 37 13 88328251 − N_Shore SLITRK5(body) cg21090457 37 3 77573709 + ROBO2(body) cg16915863 37 12 54523294 + S_Shelf LOC400043(body) cg08941355 37 7 27133106 − N_Shore HOXA1(body) cg03906434 37 7 27231819 − Island cg09823859 37 13 88328294 + N_Shore SLITRK5(body) cg05757365 37 13 88328471 + N_Shore SLITRK5(body) cg04707013 37 10 111177826 − cg23865240 37 7 27134109 + Island HOXA1(body) cg18751141 37 7 27138173 + S_Shore HOTAIRM1(body) cg24626752 37 13 88328274 + N_Shore SLITRK5(body) cg17881200 37 7 27138850 + S_Shelf HOTAIRM1(body) cg26168643 37 13 88328009 − N_Shore SLITRK5(body) cg17485838 37 7 27138712 − S_Shore HOTAIRM1(body) cg02611934 37 13 88329407 + Island SLITRK5(body) cg07278425 37 7 27137922 + S_Shore HOTAIRM1(body) cg07318204 37 4 145566441 − Island HHIP(tss1500); HHIP- AS1(body) cg00106345 37 7 27138396 + S_Shore HOTAIRM1(body) “Mean not-CHARGE” refers to the mean β-value for the CpG loci in the non-CS cases. “Mean CHARGE” refers to the mean β-value for the CpG loci in the CS samples.

TABLE 3 Cross-validation results for different effect-size (absolute delta beta, |Δβ|) thresholds at p-value <0.01. Shown are the specificity (Spec) and sensitivity (Sens) of the LOO procedure, specificity for 1056 normal blood samples derived from GEO (Spec GEO). The total number of significant sites (CGs) in the resulting “CHD7 signature” set, the gene names (Names) and their total number (Genes) corresponding to the significant sites. One optimal combination (highlighted in bold) was selected to be p-value <0.01 and |Δβ| >10%. The p-values are corrected for multiple testing (Benjamini-Hochberg correction). p-value <0.01 Spec |Δβ| Spec Sens (GEO) CGs Names Genes 5% 100.0% 100.0% 99.9% 542 ACAP2; ADAMTS17; ADCY5; ADIRF; ADORA2B; ALDH1A3; ALX3; ANK1; 224 ANO3; APP; ARHGEF15; ARHGEF4; ARPP21; ARSJ; ATXN7L1; AXIN2; BMP4; BMP5; BMP7; BMPER; BRE; BRINP1; C10orf90; C11orf88; C14orf177; C14orf64; C19orf45; C1orf53; C6orf89; CACNA1H; CADM3; CAMTA1; CCDC60; CCSER1; CD226; CD9; CLMP; CMTM7; COL11A1; COL21A1; COL4A2; COLEC12; DAB1; DAW1; DIP2C; DLC1; DMRT1; DMXL1; DNER; DOK1; EBF3; ELAVL2; EMILIN2; EPAS1; EPB41L1; ERBB2; ERC2; ERMN; EVI5; EVP1; FAM155A; FAM19A1; FAM83F; FCGRT; FGF2; FGF23; FLJ12825; FLJ39080; FLOT1; FMN2; FOXK1; FOXP1; FOXP2; FRMD3; GABBR1; GDF2; GIPC2; GJB6; GPATCH2; GPR151; GPRC5C; GRB7; GRID1; GRID2; HECW1; HHIP; HHIP-AS1; HOTAIRM1; HOXA-AS3; HOXA1; HOXA10; HOXA10-HOXA9; HOXA11; HOXA11-AS; HOXA2; HOXA4; HOXA5; HOXA6; HOXB8; HOXC4; HOXC5; HOXC6; HOXD9; HTR5A; IGF2; IGF2-AS; IGFBP5; IL20RA; INS-IGF2; ISG20; ISL1; KCNJ6; KCNQ4; KIAA0922; KIAA1161; KIRREL3; KLHL14; L3MBTL4; LAMA2; LCE3A; LHX4; LINC00554; LINC00601; LINC00982; LMO3; LOC100128239; LOC100128770; LOC100996291; LOC145845; LOC400043; LOC400456; LOC642366; LRRC4C; MAFA; MFSD1; MIR10B; MIR1284; MKS1; MLLT4-AS1; MOB2; MS4A6A; MUC21; MYO1F; NCKAP5; NFIB; NKAIN3; NKX3-1; NOX4; NPSR1; NPSR1- AS1; NR4A2; NRARP; NXN; OPCM1; OPRM1; PALM2; PALM2-AKAP2; PARVA; PCDH15; PCDH20; PDE4C; PDZRN3; PGLYRP1; PKNOX2; PLBD1; PNLIPRP3; POSTN; PRLR; PRMT8; PRSS56; PSAPL1; PTCHD4; PVRL3; PVRL3-AS1; RAB3C; RAC1; RARRES2; RBFOX3; RELN; RGS17; RGS7; RNF180; ROBO1; ROBO2; RUNX1T1; SEC24D; SGPP2; SHISA9; SLC1A3; SLC24A4; SLC35C1; SLCO1A2; SLFN12; SLITRK5; SLPI; SORCS2; SOX2-OT; SOX7; SPATA17; STEAP2; SYNE1; TBX3; TBX5; TEAD1; TENM4; TFAP2A; TMCC1; TMCC1-AS1; TMEM132C; TPO; TRUB1; TSPAN4; TTC24; TUBGCP3; UGP2; VWF; WFDC2; WNT7A; ZCCHC14; ZDHHC22; ZEB1; ZFP64; ZIC4; ZNF586 10% 100.0% 100.0% 99.5% 146 ANO3; APP; ARPP21; BMP4; BMP7; C14orf77; C6orf89; CCDC60; 44 COL11A1; COL4A2; DAB1; FMN2; FOXP2; GJB6; HHIP; HHIP-AS1; HOTAIRM1; HOXA-AS3; HOXA1; HOXA4; HOXA5; HOXA6; HOXB8; HOXC4; KIAA1161; KIRREL3; LAMA2; LOC100128770; LOC400043; MKS1; MLLT4-AS1; NOX4; NR4A2; OPCM1; PARVA; PCDH20; ROBO2; SLCO1A2; SLITRK5; SOX2-OT; TBX5; TEAD1; TTC24; VWF 15% 100.0% 100.0% 96.8% 44 APP; C14orf177; HHIP; HHIP-AS1; HOTAIRM1; HOXA- 16 AS3; HOXA1; HOXA5; HOXA6; HOXC4; KIAA1161; LOC400043; ROBO2; SLITRK5; TTC24; VWF 20% 82.2% 100.0% 87.5% 8 HHIP; HHIP-AS1; HOTAIRM1; HOXA-AS3; HOXA5; SLITRK5 6 22% 51.1% 80.0% 67.0% 3 HOXA-AS3; HOXA5 2

TABLE 4 Cross-validation results for different effect-size (absolute delta beta, |Δβ|) thresholds at p-value ≦0.001. Shown are the specificity (Spec) and sensitivity (Sens) of the LOO procedure, specificity for 1056 normal blood samples derived from GEO (Spec GEO). The total number of significant sites (CGs) in the resulting “CHD7 signature” set, the gene names (Names) and their total number (Genes) corresponding to the significant sites. The p-values are corrected for multiple testing (Benjamini-Hochberg correction). p-value ≦0.001 Spec |Δβ| Spec Sens (GEO) CGs Names Genes 5% 100.0% 100.0% 99.7% 210 ALX3; ANO3; APP; ARHGEF15; ARPP21; ARSJ; BMP7; C14orf177; C1orf53; 81 CAMTA1; COL11A1; COL4A2; COLEC12; DAB1; DLC1; EBF3; ELAVL2; EPB41L1; FAM155A; FLJ12825; FMN2; FOXP1; FOXP2; GPRC5C; HECW1; HHIP; HHIP-AS1; HOTAIRM1; HOXA- AS3; HOXA1; HOXA10; HOXA10-HOXA9; HOXA11; HOXA11- AS; HOXA2; HOXA5; HOXA6; HOXB8; IGF2; IGF2-AS; IL20RA; INS- IGF2; ISL1; KCNQ4; KIRREL3; KLHL14; LINC00554; LINC00982; LMO3; LOC400043; MIR10B; MIR1284; MKS1; MS4A6A; NFIB; NOX4; OPRM1; PARVA; PCDH20; PGLYRP1; PKNOX2; PLBD1; PVRL3; PVRL3- AS1; RELN; RGS7; ROBO2; RUNX1T1; SGPP2; SLC1A3; SLCO1A2; SLITRK5; TBX5; TEAD1; TENM4; TFAP2A; TMCC1; TMCC1- AS1; TRUB1; VWF; WFDC2 10% 100.0% 100.0% 99.4% 102 HIP; HHIP-AS1; HOTAIRM1; HOXA- 28 AS3; HOXA1; HOXA5; HOXA6; HOXB8; KIRREL3; LOC400043; MKS1; NOX4; PARVA; PCDH20; ROBO2; SLCO1A2; SLITRK5; TBX5; TEAD1; VWF 15% 100.0% 100.0% 95.3% 36 APP; C14orf177; HHIP; HHIP-AS1; HOTAIRM1; HOXA- 12 AS3; HOXA1; HOXA5; HOXA6; ROBO2; SLITRK5; VWF 20% 82.2% 100.0% 87.5% 8 HHIP; HHIP-AS1; HOTAIRM1; HOXA-AS3; HOXA5; SLITRK5 6 22% 51.1% 80.0% 67.0% 3 HOXA-AS3; HOXA5 2

TABLE 5 Cross-validation results for different effect-size (absolute delta beta, |Δβ|) thresholds at p-value ≦1e−4. Shown are the specificity (Spec) and sensitivity (Sens) of the LOO procedure, specificity for 1056 normal blood samples derived from GEO (Spec GEO). The total number of significant sites (CGs) in the resulting “CHD7 signature” set, the gene names (Names) and their total number (Genes) corresponding to the significant sites. The p-values are corrected for multiple testing (Benjamini-Hochberg correction). p-value ≦1e−4 Spec |Δβ| Spec Sens (GEO) CGs Names Genes 5% 100.0% 100.0% 98.8% 103 APP; ARSJ; C14orf177; FAM155A; FOXP2; HOTAIRM1; HOXA- 29 AS3; HOXA1; HOXA10; HOXA10-HOXA9; HOXA11; HOXA11- AS; HOXA5; HOXA6; HOXB8; IL20RA; KIRREL3; MS4A6A; NOX4; OPRM1; PARVA; PVRL3; PVRL3- AS1; RELN; ROBO2; SLCO1A2; TBX5; TEAD1; VWF 10% 100.0% 100.0% 98.5% 72 APP; C14orf177; FOXP2; HOTAIRM1; HOXA- 17 AS3; HOXA1; HOXA5; HOXA6; HOXB8; KIRREL3; NOX4; PARVA; ROBO2; SLCO1A2; TBX5; TEAD1; VWF 15% 97.8% 100.0% 90.9% 27 APP; C14orf177; HOTAIRM1; HOXA- 9 AS3; HOXA1; HOXA5; HOXA6; ROBO2; VWF 20% 75.6% 100.0% 80.0% 6 HOTAIRM1; HOXA-AS3; HOXA5 3 22% 48.9% 66.7% 67.0% 3 HOXA-AS3; HOXA5 2

TABLE 6 Cross-validation results for different effect-size (absolute delta beta, |Δβ|) thresholds at p-value ≦1e−5. Shown are the specificity (Spec) and sensitivity (Sens) of the LOO procedure, specificity for 1056 normal blood samples derived from GEO (Spec GEO). The total number of significant sites (CGs) in the resulting “CHD7 signature” set, the gene names (Names) and their total number (Genes) corresponding to the significant sites. The p-values are corrected for multiple testing (Benjamini-Hochberg correction). p-value ≦1e−5 Spec |Δβ| Spec Sens (GEO) CGs Names Genes 5% 100.0% 100.0% 97.7% 68 APP; C14orf177; FOXP2; HOTAIRM1; HOXA- 16 AS3; HOXA1; HOXA10; HOXA10- HOXA9; HOXA5; HOXA6; HOXB8; PARVA; RELN; ROBO2; TBX5; TEAD1 10% 100.0% 100.0% 97.7% 53 APP; C14orf177; FOXP2; HOTAIRM1; HOXA- 13 AS3; HOXA1; HOXA5; HOXA6; HOXB8; PARVA; ROBO2; TBX5; TEAD1 15% 93.3% 100.0% 89.4% 25 APP; C14orf177; HOTAIRM1; HOXA- 8 AS3; HOXA1; HOXA5; HOXA6; ROBO2 20% 75.6% 100.0% 80.0% 6 HOTAIRM1; HOXA-AS3; HOXA5 3 22% 48.9% 66.7% 67.0% 3 HOXA-AS3; HOXA5 2

TABLE 7 KMT2D mutation information. Kabuki Score is defined by the formula: KS score(B) = r (B, KS profile) − r (B, control profile) Sample ID Sex Nucleotide change Amino acid change Exon Inheritance Kabuki Score P1 F c.15067C > T p.R5021X 48 de novo 0.357 P2 F c.8171_8172del or 8172_8173del p.P2724Qfs*5 32 not in mom 0.324 P3 M c.6595del p.Y2199Ifs*65 31 de novo 0.414 P4 M c.14055-14056delCA p.H4685Qfs*4 43 de novo 0.472 P5 M c.6295C > T p.R2099X 31 de novo 0.250 P6 M c.4135_4136del p.M1379Vfs*52 14 de novo 0.415 P7 M c.12592C > T p.R4198X 39 de novo 0.455 P8 M c.4135_4136del p.M1379VfsX*52 14 de novo 0.462 P9 M c.11710C > T p.Q3904X 39 de novo 0.336 P10 M c.16318delG p.E5440Rfs*16 39 de novo 0.292 P11 M c15030dupA p.E5011Rfs*13 48 de novo 0.398 U1 F molecular pending −0.212 V1 F c.15143G > A p.R5048H 48 unknown 0.325 V2 M c.12028 T > C p.Ser4010Pro 39 unknown −0.346 V4 M c.15659G > A p.R5220H 48 inherited −0.308 V5 M c.10256A > G p.D3419G 35 inherited −0.266 V6 F c.8942G > A p.E2992K 34 inherited −0.349 V7 F c.8831A > G p.N2944S 34 inherited −0.384 V8 F c.832G > A p.A278T 6 inherited −0.281 V9 M c.682C > G (known SNP) p.R228G 6 inherited −0.386

TABLE 8 Cross-validation results for different combination of statistical and effect-size thresholds. Shown are the specificity (Spec) and sensitivity (Sens) of the LOO procedure, and the total number of significant sites (CGs) in the resulting “Kabuki signature” set. One optimal combination was selected to be p-value ≦0.05 and |Δβ| >15%, which led to no classification errors. Classification errors: FN = false negatives, FP = false positives. p-value ≦0.05 p-value ≦0.01 p-value ≦0.005 Db Spec FP Sens FN CGs Spec FP Sens FN CGs Spec FP Sens FN CGs  5% 1 0.91 KP10 13595 1 0.91 KP10 9993 1 0.91 KP10 8490 10% 1 0.91 KP10 1941 1 0.91 KP10 1704 1 0.91 KP10 1569 15% 1 1 287 1 1 272 1 1 267 20% 1 1 46 1 1 46 1 1 46 25% 1 0.55 KP3 KP5 KP7 KP10 KP11 10 1 0.82 KP5 KP10 10 1 0.91 KP5 10 p-value ≦0.001 p-value ≦ 0.0001 p-value ≦0.00001 Db Spec FP Sens FN CGs Spec FP Sens FN CGs Spec FP Sens FN CGs  5% 1 0.91 KP10 5492 1 1 2696 1 1 1188 10% 1 1 1248 1 1 801 1 1 447 15% 1 1 232 1 1 181 1 1 111 20% 1 1 43 1 1 39 1 1 29 25% 1 0.91 KP5 9 1 0.82 KP2 KP5 9 1 0.91 KP10 6

TABLE 9 287 CpG loci corresponding to 162 genes were identified as showing a statistically significant (p-value ≦0.05) difference in KS and non-KS controls. “Mean not-Kabuki” refers to the mean beta-value for the CpG loci in the non-KS cases. “Mean Kabuki” refers to the mean beta-value for the CpG loci in the KS samples. Benjamini- Hochberg DNA corrected p- Absolute methylation Mean not- Mean Illumina ID p-value value deltaBeta deltaBeta effect Kabuki Kabuki Gene Symbol Genome_Build cg22987448 5.37E−11 1.44E−07 −0.368 0.368 LOSS 0.857 0.490 MYO1F 37 cg15254671 2.69E−11 1.11E−07 −0.344 0.344 LOSS 0.828 0.484 MYO1F 37 cg05857996 2.03E−07 1.56E−05 −0.335 0.335 LOSS 0.693 0.358 EBF4 37 cg08283130 2.55E−10 2.92E−07 −0.280 0.280 LOSS 0.827 0.547 MYO1F 37 cg01178624 2.03E−07 1.56E−05 −0.278 0.278 LOSS 0.795 0.516 KCNK7; KCNK7; KCNK7; 37 KCNK7 cg00274965 4.44E−07 2.71E−05 −0.272 0.272 LOSS 0.361 0.089 37 cg09232555 0.000373661 0.003497351 −0.264 0.264 LOSS 0.593 0.329 C8orf49 37 cg22568423 9.40E−11 1.80E−07 −0.259 0.259 LOSS 0.793 0.534 MYO1F 37 cg08818610 9.00E−09 2.04E−06 0.259 0.259 GAIN 0.347 0.606 FAM65B; FAM65B; 37 FAM65B cg16370398 5.01E−10 4.23E−07 −0.250 0.250 LOSS 0.499 0.248 HOXC4; HOXC4 37 cg15954353 5.56E−05 0.000865565 −0.248 0.248 LOSS 0.776 0.529 LOC728392 37 cg05825244 5.49E−08 6.50E−06 −0.246 0.246 LOSS 0.332 0.086 EBF4 37 cg09226051 2.24E−06 8.63E−05 −0.243 0.243 LOSS 0.427 0.185 NLRP3; NLRP3; NLRP3; 37 NLRP3; NLRP3; NLRP3 cg21637392 2.04E−08 3.44E−06 0.239 0.239 GAIN 0.098 0.337 RNF216; RNF216 37 cg14172108 4.44E−07 2.71E−05 −0.236 0.236 LOSS 0.508 0.272 37 cg11532431 6.04E−10 4.32E−07 −0.233 0.233 LOSS 0.833 0.600 HOXA4 37 cg20543544 3.26E−09 1.18E−06 0.229 0.229 GAIN 0.294 0.523 ZMIZ1 37 cg05491854 2.04E−08 3.44E−06 0.226 0.226 GAIN 0.485 0.711 FAM65B; FAM65B; 37 FAM65B cg08255475 2.55E−10 2.92E−07 −0.226 0.226 LOSS 0.518 0.292 CDT1 37 cg08425810 3.02E−07 2.07E−05 −0.226 0.226 LOSS 0.729 0.503 AGAP2; AGAP2 37 cg22997113 9.00E−09 2.04E−06 −0.225 0.225 LOSS 0.592 0.367 HOXA4; HOXA4 37 cg15454820 5.49E−08 6.50E−06 0.224 0.224 GAIN 0.213 0.437 37 cg14911689 0.000496589 0.004303102 0.224 0.224 GAIN 0.389 0.613 NINJ2 37 cg25308803 1.57E−08 2.89E−06 −0.224 0.224 LOSS 0.622 0.398 SH3RF3; SH3RF3- 37 AS1; SH3RF3-AS1 cg10785373 6.73E−09 1.68E−06 −0.223 0.223 LOSS 0.587 0.364 37 cg23387569 4.03E−10 3.55E−07 −0.221 0.221 LOSS 0.867 0.645 AGAP2; AGAP2; AGAP2- 37 AS1 cg00313914 9.15E−07 4.51E−05 −0.220 0.220 LOSS 0.532 0.312 NAV1 37 cg03846641 3.26E−09 1.18E−06 −0.218 0.218 LOSS 0.602 0.384 SH3RF3; SH3RF3- 37 AS1; SH3RF3-AS1 cg14099457 1.19E−08 2.44E−06 0.217 0.217 GAIN 0.534 0.752 LAMB2; LAMB2 37 cg19738980 1.30E−09 6.59E−07 −0.216 0.216 LOSS 0.621 0.405 LAMA1 37 cg19142026 3.02E−07 2.07E−05 −0.215 0.215 LOSS 0.320 0.105 HOXA4; HOXA4 37 cg09549073 1.08E−07 1.02E−05 0.215 0.215 GAIN 0.589 0.803 HOXA5; HOXA5; HOXA- 37 AS3 cg04287574 3.45E−05 0.000609982 −0.213 0.213 LOSS 0.379 0.165 NAV1 37 cg03269218 9.00E−09 2.04E−06 0.211 0.211 GAIN 0.320 0.530 37 cg05905531 4.03E−10 3.55E−07 −0.207 0.207 LOSS 0.820 0.612 MYO1F 37 cg12474798 3.64E−09 1.18E−06 −0.207 0.207 LOSS 0.479 0.272 ADO 37 cg20225999 9.00E−09 2.04E−06 −0.206 0.206 LOSS 0.819 0.613 37 cg24690094 7.00E−05 0.001021959 0.206 0.206 GAIN 0.462 0.668 DOC2GP 37 cg02224314 9.00E−10 5.32E−07 0.205 0.205 GAIN 0.710 0.916 BCL11B; BCL11B; 37 BCL11B; BCL11B cg18025886 4.03E−10 3.55E−07 0.204 0.204 GAIN 0.524 0.728 MFI2; MFI2 37 cg03146625 3.64E−09 1.18E−06 −0.204 0.204 LOSS 0.573 0.369 HOXC4; HOXC4 37 cg21429551 3.39E−08 4.75E−06 −0.204 0.204 LOSS 0.504 0.301 GARS 37 cg03455316 3.45E−05 0.000609982 0.203 0.203 GAIN 0.616 0.819 37 cg06663305 1.65E−07 1.36E−05 0.203 0.203 GAIN 0.282 0.485 37 cg09817024 3.39E−08 4.75E−06 0.202 0.202 GAIN 0.178 0.379 37 cg09214243 6.13E−06 0.000175468 0.201 0.201 GAIN 0.516 0.717 37 cg01246520 7.84E−05 0.001110451 0.200 0.200 GAIN 0.529 0.729 RAI1 37 cg26404511 2.69E−11 1.11E−07 −0.199 0.199 LOSS 0.320 0.121 CNR2 37 cg15795305 9.00E−10 5.32E−07 0.198 0.198 GAIN 0.314 0.512 37 cg14018024 3.45E−05 0.000609982 −0.198 0.198 LOSS 0.721 0.523 LAMC3 37 cg20704450 3.67E−07 2.37E−05 0.198 0.198 GAIN 0.399 0.596 37 cg14759565 2.64E−08 4.05E−06 −0.197 0.197 LOSS 0.835 0.637 37 cg24263062 5.34E−07 3.08E−05 −0.197 0.197 LOSS 0.565 0.368 EBF4 37 cg26654770 0.004922514 0.022926275 0.197 0.197 GAIN 0.373 0.569 NINJ2 37 cg12128839 5.49E−08 6.50E−06 0.197 0.197 GAIN 0.621 0.818 HOXA5; HOXA5; HOXA- 37 AS3 cg04517524 6.73E−09 1.68E−06 −0.196 0.196 LOSS 0.476 0.279 ASB2; ASB2 37 cg11015251 5.34E−07 3.08E−05 −0.196 0.196 LOSS 0.461 0.265 HOXA4; HOXA4 37 cg11693285 3.89E−05 0.000666588 0.196 0.196 GAIN 0.301 0.497 37 cg24217894 1.34E−11 9.45E−08 −0.196 0.196 LOSS 0.876 0.680 AGAP2; AGAP2; AGAP2- 37 AS1 cg24680632 5.50E−08 6.51E−06 0.196 0.196 GAIN 0.239 0.435 37 cg08347626 3.67E−07 2.37E−05 0.195 0.195 GAIN 0.433 0.628 37 cg23901918 3.39E−08 4.75E−06 −0.195 0.195 LOSS 0.353 0.158 SH3PXD2A 37 cg06847624 4.33E−08 5.55E−06 −0.195 0.195 LOSS 0.315 0.121 PFN3; PFN3 37 cg03068497 5.49E−08 6.50E−06 −0.194 0.194 LOSS 0.553 0.359 GARS 37 cg00815832 1.57E−08 2.89E−06 0.194 0.194 GAIN 0.567 0.761 37 cg27403406 1.23E−05 0.000291253 −0.194 0.194 LOSS 0.659 0.465 B4GALT5 37 cg01994308 9.15E−07 4.51E−05 0.194 0.194 GAIN 0.401 0.594 CHCHD7; CHCHD7; 37 CHCHD7; CHCHD7; CHCHD7; PLAG1; PLAG1; PLAG1; CHCHD7 cg05991492 2.69E−05 0.000512459 0.193 0.193 GAIN 0.410 0.604 37 cg13379325 0.002931544 0.015698881 −0.193 0.193 LOSS 0.694 0.501 KCNQ2; KCNQ2; KCNQ2; 37 KCNQ2 cg23549902 5.56E−05 0.000865565 0.193 0.193 GAIN 0.487 0.680 ZNF890P; ZNF890P 37 cg00401101 2.11E−06 8.17E−05 −0.193 0.193 LOSS 0.432 0.239 FAM134B; FAM134B 37 cg14845962 1.61E−10 2.37E−07 −0.193 0.193 LOSS 0.936 0.744 AGAP2; AGAP2; AGAP2- 37 AS1 cg23669081 0.003163359 0.016600183 −0.192 0.192 LOSS 0.544 0.351 HOXB7 37 cg16651126 3.39E−08 4.75E−06 −0.192 0.192 LOSS 0.392 0.200 HOXA4; HOXA4 37 cg11336382 7.67E−07 3.99E−05 0.192 0.192 GAIN 0.481 0.673 37 cg00130223 3.08E−09 1.13E−06 −0.191 0.191 LOSS 0.555 0.364 37 cg06904356 3.02E−07 2.07E−05 0.191 0.191 GAIN 0.674 0.864 37 cg01238044 0.001641905 0.010412048 0.191 0.191 GAIN 0.173 0.364 GSTT1; GSTT1 37 cg07211044 5.37E−11 1.44E−07 −0.190 0.190 LOSS 0.440 0.250 TOX 37 cg24927841 1.87E−09 8.15E−07 −0.190 0.190 LOSS 0.761 0.570 37 cg10146935 2.64E−08 4.05E−06 −0.190 0.190 LOSS 0.275 0.084 SAMD11 37 cg19579217 9.00E−09 2.04E−06 0.190 0.190 GAIN 0.560 0.750 37 cg25910261 0.000109302 0.001420029 0.190 0.190 GAIN 0.281 0.471 PTPRN2; PTPRN2; 37 PTPRN2 cg17740434 2.55E−10 2.92E−07 0.190 0.190 GAIN 0.308 0.498 MIR548N; TTN- 37 AS1; TTN-AS1 cg02919082 0.001110315 0.007731233 −0.189 0.189 LOSS 0.481 0.291 HLA-DQA1 37 cg02616966 4.29E−05 0.000728646 0.189 0.189 GAIN 0.059 0.249 MCCC1; MCCC1 37 cg11510586 1.61E−05 0.000353081 0.188 0.188 GAIN 0.258 0.447 37 cg20100745 0.000206017 0.002263976 −0.188 0.188 LOSS 0.459 0.271 NDRG1; NDRG1; NDRG1; 37 NDRG1 cg02715602 4.33E−08 5.55E−06 −0.188 0.188 LOSS 0.850 0.663 SEMA6B 37 cg07599786 3.38E−06 0.000114817 −0.187 0.187 LOSS 0.544 0.357 NAV1 37 cg16423910 9.40E−11 1.80E−07 0.186 0.186 GAIN 0.345 0.531 CD37; CD37 37 cg08911368 6.91E−08 7.57E−06 0.186 0.186 GAIN 0.142 0.328 37 cg03930209 1.30E−09 6.59E−07 0.185 0.185 GAIN 0.617 0.802 37 cg16440561 2.48E−06 9.16E−05 −0.185 0.185 LOSS 0.277 0.092 SPEG 37 cg23489137 0.001008525 0.007271153 −0.185 0.185 LOSS 0.530 0.345 RBMS1; RBMS1 37 cg02639108 3.38E−06 0.000114817 −0.185 0.185 LOSS 0.731 0.547 37 cg11410718 5.34E−07 3.08E−05 −0.184 0.184 LOSS 0.412 0.228 HOXA4; HOXA4 37 cg05463589 1.34E−07 1.17E−05 0.183 0.183 GAIN 0.623 0.806 IL17C 37 cg16823042 2.51E−10 2.92E−07 −0.183 0.183 LOSS 0.718 0.535 AGAP2; AGAP2; AGAP2- 37 AS1 cg03613822 8.16E−06 0.000215408 −0.183 0.183 LOSS 0.662 0.479 DLG4; DLG4 37 cg24652615 1.80E−06 7.28E−05 −0.183 0.183 LOSS 0.682 0.500 TMEM151B 37 cg16565409 1.30E−09 6.59E−07 −0.182 0.182 LOSS 0.512 0.330 RPL23A; SNORD4 37 cg13518079 2.62E−09 9.86E−07 −0.182 0.182 LOSS 0.276 0.094 EBF4 37 cg02892925 2.69E−11 1.11E−07 −0.182 0.182 LOSS 0.636 0.454 TOX 37 cg17569124 1.65E−07 1.36E−05 0.181 0.181 GAIN 0.625 0.806 HOXA5; HOXA- 37 AS3 cg19196401 8.16E−06 0.000215408 0.181 0.181 GAIN 0.645 0.826 DDO; DDO 37 cg13068698 1.02E−05 0.000257113 −0.181 0.181 LOSS 0.414 0.233 DPY19L1 37 cg07317062 1.29E−06 5.75E−05 −0.181 0.181 LOSS 0.397 0.217 HOXA4; HOXA4 37 cg10648815 5.34E−07 3.08E−05 −0.180 0.180 LOSS 0.647 0.467 LAIR2; LAIR2 37 cg03651054 0.002512273 0.014035499 −0.180 0.180 LOSS 0.620 0.440 37 cg16814680 0.006594781 0.028440415 −0.180 0.180 LOSS 0.525 0.345 37 cg12097883 1.61E−10 2.37E−07 −0.180 0.180 LOSS 0.293 0.113 LOC146880; LOC146880 37 cg23061725 1.19E−08 2.44E−06 0.179 0.179 GAIN 0.344 0.523 CASP8; CASP8; CASP8; 37 CASP8; CASP8; CASP8 cg14359292 4.44E−07 2.71E−05 −0.179 0.179 LOSS 0.322 0.143 HOXA4 37 cg18424841 5.34E−07 3.08E−05 −0.179 0.179 LOSS 0.739 0.560 37 cg04991337 3.45E−05 0.000609982 0.178 0.178 GAIN 0.106 0.284 MCCC1; MCCC1 37 cg02439789 1.52E−06 6.46E−05 −0.178 0.178 LOSS 0.472 0.294 SAMD11 37 cg01948217 1.08E−07 1.02E−05 0.178 0.178 GAIN 0.243 0.421 BPI 37 cg12748890 2.48E−06 9.16E−05 −0.178 0.178 LOSS 0.732 0.554 SYTL1; SYTL1 37 cg11969813 1.08E−07 1.02E−05 −0.178 0.178 LOSS 0.821 0.644 P4HB 37 cg18977541 4.33E−08 5.55E−06 0.177 0.177 GAIN 0.169 0.347 37 cg16734913 5.56E−05 0.000865565 −0.177 0.177 LOSS 0.665 0.487 OR5W2 37 cg22220710 2.04E−08 3.44E−06 −0.177 0.177 LOSS 0.605 0.428 LAMA1 37 cg03604073 6.41E−07 3.50E−05 0.177 0.177 GAIN 0.243 0.420 ARHGAP35 37 cg25513090 8.66E−08 8.79E−06 −0.176 0.176 LOSS 0.702 0.526 DAGLB; DAGLB 37 cg26823666 1.65E−07 1.36E−05 0.176 0.176 GAIN 0.316 0.492 37 cg20016023 9.00E−10 5.32E−07 −0.176 0.176 LOSS 0.501 0.325 RRP12; RRP12; RRP12 37 cg24753998 6.13E−06 0.000175468 0.176 0.176 GAIN 0.416 0.593 MAP3K7CL; MAP3K7CL; 37 MAP3K7CL; MAP3K7CL; MAP3K7CL; MAP3K7CL cg26371957 0.005673932 0.025448023 0.176 0.176 GAIN 0.467 0.643 NINJ2 37 cg25307665 1.80E−06 7.28E−05 0.174 0.174 GAIN 0.654 0.828 HOXA5; HOXA- 37 AS3 cg20978937 6.91E−08 7.57E−06 −0.174 0.174 LOSS 0.535 0.361 PLD4 37 cg08234664 1.29E−06 5.75E−05 0.174 0.174 GAIN 0.499 0.674 LAMB2; LAMB2 37 cg13619522 2.54E−08 4.05E−06 −0.174 0.174 LOSS 0.747 0.573 CSK; CSK 37 cg20698421 4.33E−08 5.55E−06 −0.174 0.174 LOSS 0.555 0.381 SLC1A4; SLC1A4 37 cg11511175 7.52E−10 5.32E−07 −0.173 0.173 LOSS 0.752 0.578 AGAP2; AGAP2; AGAP2- 37 AS1 cg27001715 6.83E−08 7.57E−06 −0.173 0.173 LOSS 0.552 0.379 37 cg19916659 1.80E−06 7.28E−05 0.173 0.173 GAIN 0.295 0.467 MIR548N; TTN- 37 AS1; TTN-AS1 cg07616871 2.62E−09 9.86E−07 −0.173 0.173 LOSS 0.639 0.467 37 cg21476494 3.64E−09 1.18E−06 0.172 0.172 GAIN 0.360 0.532 37 cg20007021 1.29E−06 5.75E−05 0.172 0.172 GAIN 0.233 0.405 LTB4R; LTB4R2; CIDEB; 37 CIDEB; LTB4R2 cg10044179 0.002714796 0.014844937 −0.171 0.171 LOSS 0.461 0.289 ANKRD20A11P 37 cg12176783 7.67E−07 3.99E−05 −0.171 0.171 LOSS 0.651 0.480 TCEA2; TCEA2 37 cg02954987 1.65E−07 1.36E−05 0.171 0.171 GAIN 0.583 0.754 LAMB2; LAMB2 37 cg21570209 5.30E−06 0.000157903 0.171 0.171 GAIN 0.490 0.661 FOXA3; SYMPK 37 cg24937727 1.08E−07 1.02E−05 −0.171 0.171 LOSS 0.265 0.094 RGL3; RGL3 37 cg22582187 6.73E−09 1.68E−06 −0.171 0.171 LOSS 0.760 0.589 37 cg03043406 1.34E−11 9.45E−08 −0.170 0.170 LOSS 0.614 0.444 RPS8; SNORD38A 37 cg09636302 2.11E−06 8.17E−05 −0.170 0.170 LOSS 0.734 0.564 HAL; HAL; HAL 37 cg24550112 3.64E−09 1.18E−06 −0.170 0.170 LOSS 0.337 0.167 PRDM2 37 cg17900689 5.49E−08 6.50E−06 −0.170 0.170 LOSS 0.668 0.499 ZMYND15; ZMYND15; 37 ZMYND15 cg24524285 1.19E−08 2.44E−06 −0.170 0.170 LOSS 0.652 0.482 NRXN2; NRXN2; NRXN2 37 cg17655970 9.79E−05 0.001309695 0.169 0.169 GAIN 0.293 0.462 37 cg24194775 1.84E−05 0.000387482 −0.169 0.169 LOSS 0.546 0.377 NPR2 37 cg04387835 0.000150821 0.001799142 −0.169 0.169 LOSS 0.463 0.294 ZMYND15; ZMYND15; 37 ZMYND15 cg26411441 6.91E−08 7.57E−06 −0.168 0.168 LOSS 0.458 0.289 HSPA12B; HSPA12B 37 cg24517467 1.52E−06 6.46E−05 0.168 0.168 GAIN 0.458 0.627 37 cg08657492 3.02E−07 2.07E−05 −0.168 0.168 LOSS 0.510 0.341 HOXA4 37 cg17431280 9.79E−05 0.001309695 0.168 0.168 GAIN 0.184 0.352 ARHGAP35 37 cg20748533 2.90E−06 0.000102762 −0.168 0.168 LOSS 0.455 0.287 SHANK1 37 cg21111256 0.007480707 0.031135633 0.168 0.168 GAIN 0.320 0.489 CYP2A7; CYP2A7 37 cg06768599 1.34E−11 9.45E−08 −0.168 0.168 LOSS 0.951 0.783 LTB4R; LTB4R 37 cg18090145 1.52E−06 6.46E−05 −0.168 0.168 LOSS 0.712 0.544 37 cg00343839 7.08E−06 0.000194415 −0.168 0.168 LOSS 0.337 0.169 LOC728392 37 cg00290607 9.79E−05 0.001309695 0.168 0.168 GAIN 0.640 0.807 DOC2GP 37 cg00011856 8.66E−08 8.79E−06 −0.168 0.168 LOSS 0.528 0.360 IGFBP5 37 cg24940967 1.19E−08 2.44E−06 −0.167 0.167 LOSS 0.455 0.288 SLC6A20; SLC6A20 37 cg15265092 6.24E−05 0.000941319 −0.167 0.167 LOSS 0.640 0.473 SNRPC; SNRPC 37 cg25288140 6.82E−05 0.001021959 −0.167 0.167 LOSS 0.799 0.633 BRCA1; BRCA1; BRCA1; 37 BRCA1; NBR2; BRCA1 cg19786602 0.000150821 0.001799142 −0.167 0.167 LOSS 0.571 0.404 37 cg13614409 7.84E−05 0.001110451 −0.167 0.167 LOSS 0.611 0.445 TEX26; TEX26- 37 AS1; TEX26- AS1; TEX26- AS1; TEX26-AS1 cg27539527 1.61E−10 2.37E−07 0.167 0.167 GAIN 0.484 0.651 37 cg01834979 1.61E−10 2.37E−07 −0.166 0.166 LOSS 0.838 0.671 AGAP2; AGAP2; AGAP2- 37 AS1 cg23060513 3.94E−06 0.000127744 −0.166 0.166 LOSS 0.767 0.601 FARSA 37 cg08739651 2.55E−10 2.92E−07 0.166 0.166 GAIN 0.229 0.395 FLJ31813 37 cg08355456 3.05E−05 0.000559351 0.166 0.166 GAIN 0.498 0.664 DOC2GP 37 cg18322589 4.33E−08 5.55E−06 0.166 0.166 GAIN 0.713 0.879 TACC2; TACC2 37 cg24576298 3.02E−07 2.07E−05 −0.166 0.166 LOSS 0.632 0.466 PNPLA8; PNPLA8; 37 PNPLA8; PNPLA8; PNPLA8; PNPLA8 cg06015422 0.001919305 0.011652497 0.166 0.166 GAIN 0.293 0.459 37 cg22259797 0.001982236 0.011800854 −0.165 0.165 LOSS 0.532 0.366 C2CD2L 37 cg18587137 3.29E−08 4.75E−06 −0.165 0.165 LOSS 0.882 0.716 TNFAIP2 37 cg18784409 4.44E−07 2.71E−05 −0.165 0.165 LOSS 0.517 0.352 CHKA; CHKA 37 cg13759905 3.64E−09 1.18E−06 −0.164 0.164 LOSS 0.426 0.262 37 cg09284949 1.65E−07 1.36E−05 −0.164 0.164 LOSS 0.250 0.086 SHANK1 37 cg03701930 5.30E−06 0.000157903 −0.164 0.164 LOSS 0.259 0.095 37 cg03691722 1.65E−07 1.36E−05 −0.164 0.164 LOSS 0.666 0.502 LAMA1 37 cg27466845 4.33E−08 5.55E−06 −0.164 0.164 LOSS 0.819 0.656 NRXN2; NRXN2; NRXN2 37 cg16915863 4.44E−07 2.71E−05 −0.163 0.163 LOSS 0.772 0.608 LOC400043 37 cg12133451 6.24E−05 0.000941319 0.163 0.163 GAIN 0.614 0.778 37 cg06137123 0.000109302 0.001420029 −0.163 0.163 LOSS 0.741 0.578 37 cg10501093 1.19E−08 2.44E−06 −0.163 0.163 LOSS 0.919 0.756 TNFAIP2 37 cg08801017 8.16E−06 0.000215408 −0.163 0.163 LOSS 0.504 0.341 ANKRD26P3; LINC00421 37 cg16312514 0.00059748 0.004922032 −0.162 0.162 LOSS 0.503 0.341 SHANK2 37 cg02666610 1.23E−05 0.000291253 −0.162 0.162 LOSS 0.284 0.122 37 cg19321684 2.64E−08 4.05E−06 0.162 0.162 GAIN 0.319 0.481 GPSM3; GPSM3 37 cg05076221 4.33E−08 5.55E−06 0.161 0.161 GAIN 0.570 0.731 HOXA5; HOXA- 37 AS3 cg23502204 0.000278724 0.002820594 −0.161 0.161 LOSS 0.666 0.504 RAB38 37 cg20299697 3.94E−06 0.000127744 −0.161 0.161 LOSS 0.618 0.457 MRAS; MRAS; MRAS; 37 MRAS; MRAS; MRAS cg07040013 0.000109302 0.001420029 0.161 0.161 GAIN 0.589 0.751 37 cg07509935 2.48E−06 9.16E−05 0.161 0.161 GAIN 0.239 0.400 LTB4R; LTB4R2; CIDEB; 37 LTB4R2 cg01231141 3.38E−06 0.000114817 0.161 0.161 GAIN 0.512 0.673 ADAMTS2; ADAMTS2 37 cg22127848 9.38E−06 0.000238239 −0.161 0.161 LOSS 0.672 0.511 37 cg04015962 1.84E−05 0.000387482 −0.160 0.160 LOSS 0.706 0.546 37 cg05226335 0.000307638 0.003033562 −0.160 0.160 LOSS 0.637 0.477 CTTN; CTTN; CTTN 37 cg24680439 0.000167527 0.00194356 0.160 0.160 GAIN 0.649 0.809 LOC399829 37 cg00497905 6.13E−06 0.000175468 −0.160 0.160 LOSS 0.440 0.281 MYO7A; MYO7A 37 cg23752752 5.49E−08 6.50E−06 0.160 0.160 GAIN 0.395 0.555 FOXK1 37 cg11210343 2.69E−11 1.11E−07 −0.159 0.159 LOSS 0.405 0.246 METAP2 37 cg07512361 7.67E−07 3.99E−05 −0.159 0.159 LOSS 0.628 0.469 SH2B2 37 cg05351887 7.00E−05 0.001021959 0.159 0.159 GAIN 0.350 0.509 37 cg01119278 9.38E−06 0.000238239 0.159 0.159 GAIN 0.613 0.772 DDO; DDO 37 cg19092981 0.000252271 0.00262715 −0.159 0.159 LOSS 0.582 0.423 TBX1; TBX1; TBX1 37 cg04799958 4.33E−08 5.55E−06 0.159 0.159 GAIN 0.625 0.784 KRT18; KRT8; KRT8; 37 KRT18; KRT8; KRT8 cg19566405 0.001018834 0.007271153 0.159 0.159 GAIN 0.205 0.364 SLFN12 37 cg26995224 3.39E−08 4.75E−06 0.159 0.159 GAIN 0.578 0.737 KDM2B; KDM2B 37 cg22841667 2.48E−07 1.81E−05 −0.159 0.159 LOSS 0.407 0.249 RPL27A; SNORA3; 37 SNORA45 cg07816074 1.57E−08 2.89E−06 0.158 0.158 GAIN 0.367 0.525 SH3TC1 37 cg22992730 0.001156175 0.008041808 0.158 0.158 GAIN 0.494 0.652 37 cg05164926 5.34E−07 3.08E−05 −0.158 0.158 LOSS 0.291 0.133 KCTD11 37 cg01287088 1.57E−08 2.89E−06 −0.158 0.158 LOSS 0.668 0.510 PFN3 37 cg06430632 1.61E−05 0.000353081 −0.158 0.158 LOSS 0.604 0.446 SFT2D1 37 cg21697381 0.00059748 0.004922032 0.157 0.157 GAIN 0.225 0.383 SLFN12 37 cg10885151 1.65E−07 1.36E−05 0.157 0.157 GAIN 0.176 0.333 37 cg11057824 3.16E−07 2.15E−05 −0.157 0.157 LOSS 0.663 0.507 C14orf182 37 cg06314111 1.62E−09 8.07E−07 −0.157 0.157 LOSS 0.765 0.608 AGAP2; AGAP2; AGAP2- 37 AS1 cg00551910 2.03E−07 1.56E−05 −0.157 0.157 LOSS 0.542 0.386 CCDC177 37 cg02784823 9.38E−06 0.000238239 0.157 0.157 GAIN 0.695 0.852 LMTK3 37 cg04220104 2.64E−08 4.05E−06 0.157 0.157 GAIN 0.408 0.565 MIR548N; TTN- 37 AS1; TTN-AS1 cg11123440 0.002714796 0.014844937 −0.156 0.156 LOSS 0.659 0.503 C8orf49 37 cg27246571 1.19E−08 2.44E−06 −0.156 0.156 LOSS 0.768 0.612 HAL; HAL; HAL 37 cg14851700 3.45E−05 0.000609982 −0.156 0.156 LOSS 0.466 0.310 GLUL; GLUL 37 cg05396897 4.39E−05 0.000728646 −0.156 0.156 LOSS 0.390 0.234 NLRP3; NLRP3; NLRP3; 37 NLRP3; NLRP3; NLRP3 cg00873601 5.49E−08 6.50E−06 0.156 0.156 GAIN 0.333 0.489 37 cg04863892 1.08E−07 1.02E−05 0.156 0.156 GAIN 0.680 0.835 HOXA5; HOXA5; HOXA- 37 AS3 cg13904806 4.57E−06 0.00014225 −0.156 0.156 LOSS 0.922 0.766 SAMD11 37 cg08610426 0.000109302 0.001420029 0.156 0.156 GAIN 0.469 0.624 IZUMO1 37 cg01837362 3.45E−05 0.000609982 −0.156 0.156 LOSS 0.533 0.378 37 cg18282375 2.55E−10 2.92E−07 −0.156 0.156 LOSS 0.586 0.430 HSPA12B; HSPA12B 37 cg14920846 6.13E−06 0.000175468 −0.155 0.155 LOSS 0.440 0.285 NAV1 37 cg09320662 2.03E−07 1.56E−05 0.155 0.155 GAIN 0.408 0.563 LRCOL1 37 cg03775991 2.03E−07 1.56E−05 0.155 0.155 GAIN 0.632 0.787 37 cg13750264 0.004580487 0.021752094 −0.155 0.155 LOSS 0.600 0.445 GPR123 37 cg23188684 4.39E−05 0.000728646 0.155 0.155 GAIN 0.466 0.621 DOC2GP 37 cg01331992 2.62E−09 9.86E−07 −0.155 0.155 LOSS 0.551 0.396 RPS6 37 cg19827875 3.39E−08 4.75E−06 −0.155 0.155 LOSS 0.932 0.777 NAV1 37 cg04865726 7.67E−07 3.99E−05 −0.155 0.155 LOSS 0.347 0.192 37 cg14898243 6.91E−08 7.57E−06 −0.155 0.155 LOSS 0.795 0.641 SRGN; SRGN 37 cg06576532 0.000185879 0.002097946 −0.155 0.155 LOSS 0.565 0.411 37 cg00011924 2.28E−05 0.000463162 −0.155 0.155 LOSS 0.522 0.367 RNF222; RNF222 37 cg03463411 0.001430091 0.009288043 0.155 0.155 GAIN 0.400 0.555 PRDM8; PRDM8 37 cg05836043 5.34E−07 3.08E−05 −0.155 0.155 LOSS 0.659 0.505 LAMA1 37 cg13541527 6.73E−09 1.68E−06 −0.154 0.154 LOSS 0.427 0.272 C6orf48; C6orf48; 37 SNORD52 cg03415617 1.08E−05 0.000263209 −0.154 0.154 LOSS 0.647 0.493 37 cg22970003 0.000121826 0.001535447 0.154 0.154 GAIN 0.263 0.417 PTPRN2; PTPRN2; 37 PTPRN2 cg01413354 0.000339214 0.003261865 0.154 0.154 GAIN 0.418 0.572 RALGDS 37 cg17624673 1.65E−07 1.36E−05 −0.154 0.154 LOSS 0.659 0.505 PCDHB13 37 cg10323490 1.19E−08 2.44E−06 −0.154 0.154 LOSS 0.762 0.609 THNSL2; THNSL2 37 cg26056277 0.004580487 0.021752094 −0.154 0.154 LOSS 0.605 0.451 SCN1A 37 cg07637837 3.05E−05 0.000559351 −0.153 0.153 LOSS 0.671 0.517 MBP; MBP 37 cg17187762 0.00041121 0.003747585 0.153 0.153 GAIN 0.577 0.731 37 cg09748975 6.41E−07 3.50E−05 −0.153 0.153 LOSS 0.411 0.258 MSX1 37 cg09652312 4.33E−08 5.55E−06 0.153 0.153 GAIN 0.542 0.695 37 cg19937979 0.000339214 0.003261865 −0.153 0.153 LOSS 0.535 0.382 CCDC177 37 cg22410743 2.11E−06 8.17E−05 −0.153 0.153 LOSS 0.778 0.626 IQCH; IQCH; IQCH; 37 IQCH; IQCH cg00344445 1.08E−05 0.000263209 −0.153 0.153 LOSS 0.783 0.630 FLI1; FLI1; FLI1; FLI1 37 cg14573099 9.00E−10 5.32E−07 −0.152 0.152 LOSS 0.742 0.590 TBC1D8 37 cg16322792 0.009765492 0.037891033 −0.152 0.152 LOSS 0.488 0.335 ZNF697 37 cg16875104 4.33E−08 5.55E−06 −0.152 0.152 LOSS 0.454 0.302 GARS 37 cg10431713 0.00041121 0.003747585 0.152 0.152 GAIN 0.143 0.294 SLFN12 37 cg26679004 2.48E−07 1.81E−05 0.152 0.152 GAIN 0.298 0.450 GRID1 37 cg14371731 2.03E−07 1.56E−05 0.152 0.152 GAIN 0.026 0.178 ZMIZ1 37 cg10213542 2.90E−06 0.000102762 0.152 0.152 GAIN 0.374 0.525 ADAMTS2; ADAMTS2 37 cg06473363 1.19E−08 2.44E−06 −0.151 0.151 LOSS 0.770 0.618 GPANK1; GPANK1; 37 GPANK1; GPANK1; GPANK1 cg02734505 1.08E−07 1.02E−05 −0.151 0.151 LOSS 0.424 0.273 ZNF385A; ZNF385A; 37 ZNF385A cg15233961 1.57E−08 2.89E−06 0.151 0.151 GAIN 0.415 0.566 37 cg06470855 1.08E−05 0.000263209 0.151 0.151 GAIN 0.669 0.820 37 cg26135325 1.29E−06 5.75E−05 −0.151 0.151 LOSS 0.351 0.200 LCE3A 37 cg24049888 1.41E−05 0.000320585 0.151 0.151 GAIN 0.319 0.470 POU2AF1; POU2AF1 37 cg20088245 0.00041121 0.003747585 0.151 0.151 GAIN 0.565 0.716 37 cg03128011 0.000185879 0.002097946 0.151 0.151 GAIN 0.585 0.736 37 cg24852442 6.41E−07 3.50E−05 −0.151 0.151 LOSS 0.417 0.266 MYO7A; MYO7A 37 cg19276111 4.33E−08 5.55E−06 −0.150 0.150 LOSS 0.358 0.208 CNR2 37 cg00693004 0.000514392 0.004451981 −0.150 0.150 LOSS 0.676 0.525 NMT1 37 cg16194588 1.52E−06 6.46E−05 0.150 0.150 GAIN 0.669 0.819 LMTK3 37 cg08551532 0.000167527 0.00194356 −0.150 0.150 LOSS 0.276 0.126 DLL3; DLL3 37 cg16481961 0.000121826 0.001535447 0.150 0.150 GAIN 0.125 0.275 MIR596 37 cg20907614 0.000109302 0.001420029 −0.150 0.150 LOSS 0.689 0.538 37 cg20806296 3.64E−09 1.18E−06 −0.150 0.150 LOSS 0.664 0.514 37 Genomic Relation to Coordinate transcription Illumina ID Chromosome (NCBI, hg19) Strand Relation_to_UCSC_CpG_Island start site (TSS) cg22987448 19 8591364 F Island MYO1F(body) cg15254671 19 8591513 F Island MYO1F(body) cg05857996 20 2675418 F S_Shore EBF4(body) cg08283130 19 8591776 R Island MYO1F(body) cg01178624 11 65360327 R Island KCNK7(body) cg00274965 21 34405681 F Island cg09232555 8 11619866 R C8orf49(body) cg22568423 19 8590567 F N_Shore MYO1F(body) cg08818610 6 24910720 F Island FAM65B(body) cg16370398 12 54448913 F S_Shore HOXC4(body) cg15954353 17 5403337 F Island LOC728392(body) cg05825244 20 2730488 F Island EBF4(body) cg09226051 1 247611502 R N_Shelf NLRP3(body) cg21637392 7 5735123 R RNF216(body) cg14172108 21 34405553 R N_Shore cg11532431 7 27169674 F Island HOXA4(body) cg20543544 10 81003657 R Island ZMIZ1(body) cg05491854 6 24910562 F N_Shore FAM65B(body) cg08255475 16 88871329 R N_Shore CDT1(body) cg08425810 12 58132558 R Island AGAP2(body); AGAP2 (tss1500) cg22997113 7 27170241 R Island HOXA4(body) cg15454820 10 96990858 F cg14911689 12 739980 F NINJ2(body) cg25308803 2 109746735 F Island SH3RF3(body); SH3RF3- AS1(tss200) cg10785373 7 4456119 F cg23387569 12 58120011 R Island AGAP2(body); AGAP2- AS1(tss200) cg00313914 1 201618901 R Island NAV1(body) cg03846641 2 109746751 F Island SH3RF3(body); SH3RF3- AS1(tss200) cg14099457 3 49170794 R LAMB2(tss200) cg19738980 18 7011463 F Island LAMA1(body) cg19142026 7 27170394 R Island HOXA4(body) cg09549073 7 27183274 F Island HOXA- AS3(body); HOXA5 (body) cg04287574 1 201619622 R Island NAV1(body) cg03269218 10 96990700 F cg05905531 19 8591721 F Island MYO1F(body) cg12474798 10 64565772 R Island ADO(body) cg20225999 2 218843435 F N_Shore cg24690094 11 67383802 R Island DOC2GP(tss1500) cg02224314 14 99641151 R Island BCL11B(body) cg18025886 3 196750939 R N_Shelf MFI2(body) cg03146625 12 54448729 F S_Shore HOXC4(body) cg21429551 7 30635762 F S_Shore GARS(body) cg03455316 15 62516405 R Island cg06663305 17 8095813 R S_Shelf cg09817024 8 11471395 R S_Shore cg09214243 15 29968124 R S_Shore cg01246520 17 17644344 F RAI1(body) cg26404511 1 24229575 R S_Shore CNR2(body) cg15795305 10 102381344 R cg14018024 9 133908909 R N_Shelf LAMC3(body) cg20704450 1 228658371 F N_Shore cg14759565 11 65360123 R Island cg24263062 20 2730191 F Island EBF4(body) cg26654770 12 740100 F NINJ2(body) cg12128839 7 27183436 R Island HOXA- AS3(body); HOXA5 (tss200) cg04517524 14 94405342 F Island ASB2(body) cg11015251 7 27170554 F Island HOXA4(tss200) cg11693285 10 131927345 R Island cg24217894 12 58120635 F Island AGAP2(body); AGAP2- AS1(body) cg24680632 12 116044032 R cg08347626 5 1850140 F N_Shore cg23901918 10 105420747 F Island SH3PXD2A(body) cg06847624 5 176827671 R Island PFN3(tss200) cg03068497 7 30635838 R S_Shore GARS(body) cg00815832 1 228658973 F Island cg27403406 20 48325721 R N_Shelf B4GALT5(body) cg01994308 8 57122990 F N_Shore CHCHD7(tss1500); PLAG1(body) cg05991492 16 3988700 R N_Shore cg13379325 20 62052259 R Island KCNQ2(body) cg23549902 7 5184155 F Island ZNF890P(body) cg00401101 5 16509323 F FAM134B(body); FAM134B (tss1500) cg14845962 12 58120237 R Island AGAP2(body); AGAP2- AS1(body) cg23669081 17 46685353 R Island HOXB7(body) cg16651126 7 27170552 F Island HOXA4(tss200) cg11336382 1 228658646 R N_Shore cg00130223 16 33070551 F Island cg06904356 5 1849983 R N_Shore cg01238044 22 24384105 F N_Shore GSTT1(body) cg07211044 8 60032983 R S_Shore TOX(tss1500) cg24927841 8 129702875 R cg10146935 1 871308 R Island SAMD11(body) cg19579217 6 10720630 R N_Shelf cg25910261 7 157405965 F Island PTPRN2(body) cg17740434 2 179388064 F MIR548N(body); TTN- AS1(body) cg02919082 6 32605694 F HLA-DQA1(body) cg02616966 3 182817190 F Island MCCC1(body) cg11510586 9 72027409 R Island cg20100745 8 134307728 F N_Shore NDRG1(body) cg02715602 19 4544446 F Island SEMA6B(body) cg07599786 1 201618654 F Island NAV1(body) cg16423910 19 49843627 F Island CD37(body) cg08911368 8 11471085 R Island cg03930209 7 156735466 R Island cg16440561 2 220312854 F Island SPEG(body) cg23489137 2 161290449 R RBMS1(body) cg02639108 2 242711009 R Island cg11410718 7 27170412 R Island HOXA4(tss200) cg05463589 16 88706426 F Island IL17C(body) cg16823042 12 58119992 R Island AGAP2(body); AGAP2- AS1(tss200) cg03613822 17 7115140 R N_Shelf DLG4(body) cg24652615 6 44243304 R Island TMEM151B(body) cg16565409 17 27048223 R S_Shore RPL23A(body); SNOARD4A (tss1500) cg13518079 20 2675072 R S_Shore EBF4(body) cg02892925 8 60032926 R S_Shore TOX(tss1500) cg17569124 7 27183643 R Island HOXA- AS3(body); HOXA5 (tss1500) cg19196401 6 110721138 R Island DDO(body) cg13068698 7 35078082 F S_Shore DPY19L1(tss1500) cg07317062 7 27170388 R Island HOXA4(body) cg10648815 19 55013549 R LAIR2(tss1500) cg03651054 13 50194643 F cg16814680 8 91681699 F cg12097883 17 62774939 R Island LOC146880(body) cg23061725 2 202126379 R CASP8(body) cg14359292 7 27170892 F S_Shore HOXA4(tss1500) cg18424841 20 61315444 F Island cg04991337 3 182817223 F Island MCCC1(body) cg02439789 1 871441 R Island SAMD11(body) cg01948217 20 36932385 F BPI(tss200) cg12748890 1 27676205 F Island SYTL1(body) cg11969813 17 79816559 R N_Shore P4HB(body) cg18977541 10 102381532 R cg16734913 11 55681277 F OR5W2(body) cg22220710 18 7011217 F N_Shore LAMA1(body) cg03604073 19 47507409 R Island ARHGAP35(body) cg25513090 7 6488668 F S_Shore DAGLB(tss1500) cg26823666 1 228658397 F N_Shore cg20016023 10 99160130 R N_Shore RRP12(body) cg24753998 21 30452964 R MAP3K7CL(body) cg26371957 12 739280 F NINJ2(body) cg25307665 7 27183694 R Island HOXA- AS3(body); HOXA5 (tss1500) cg20978937 14 105399321 R Island PLD4(body) cg08234664 3 49170668 F LAMB2(tss200) cg13619522 15 75095171 R N_Shore CSK(body) cg20698421 2 65217623 F S_Shore SLC1A4(body) cg11511175 12 58119979 R Island AGAP2(body); AGAP2- AS1(tss200) cg27001715 6 150329845 R S_Shelf cg19916659 2 179387931 R MIR548N(body); TTN- AS1(body) cg07616871 2 218843504 F Island cg21476494 12 116043958 R cg20007021 14 24780404 F Island CIDEB(body); LTB4R (tss1500); LTB4R2 (body) cg10044179 21 15352983 F S_Shore ANKRD20A11P(tss 1500) cg12176783 20 62694000 F Island TCEA2(body); TCEA2 (tss200) cg02954987 3 49170599 F LAMB2(body) cg21570209 19 46367987 R S_Shore FOXA3(body); SYMPK (tss1500) cg24937727 19 11517079 F Island RGL3(body) cg22582187 10 63394414 F cg03043406 1 45242356 R S_Shore RPS8(body); SNORD38A (tss1500) cg09636302 12 96389483 F Island HAL(body) cg24550112 1 14027521 R S_Shore PRDM2(body) cg17900689 17 4649262 F ZMYND15(body) cg24524285 11 64405919 R Island NRXN2(body) cg17655970 13 112985463 R Island cg24194775 9 35791475 R N_Shore NPR2(tss1500) cg04387835 17 4649076 F ZMYND15(body) cg26411441 20 3733040 R S_Shore HSPA12B(body) cg24517467 7 155284331 R Island cg08657492 7 27170832 F S_Shore HOXA4(tss1500) cg17431280 19 47507461 R Island ARHGAP35(body) cg20748533 19 51189975 R Island SHANK1(body) cg21111256 19 41386507 R Island CYP2A7(body) cg06768599 14 24785488 R Island LTB4R(body) cg18090145 6 67741714 F cg00343839 17 5403516 F Island LOC728392(body) cg00290607 11 67383545 R Island DOC2GP(tss1500) cg00011856 2 217560946 R S_Shore IGFBP5(tss1500) cg24940967 3 45837197 R N_Shore SLC6A20(body) cg15265092 6 34723499 F N_Shore SNRPC(tss1500) cg25288140 17 41278341 F Island BRCA1(tss1500); NBR2 (body) cg19786602 17 7966326 F cg13614409 13 31506752 F TEX26(tss200); TEX26- AS1(tss200) cg27539527 7 156735656 R Island cg01834979 12 58119918 F Island AGAP2(body); AGAP2- AS1(tss200) cg23060513 19 13041124 F N_Shelf FARSA(body) cg08739651 10 51784888 R S_Shore FLJ31813(body) cg08355456 11 67383691 R Island DOC2GP(tss1500) cg18322589 10 123909456 F TACC2(body) cg24576298 7 108137995 F PNPLA8(body) cg06015422 8 70907139 F cg22259797 11 118986860 F C2CD2L(body) cg18587137 14 103593503 R Island TNFAIP2(body) cg18784409 11 67868331 F CHKA(body) cg13759905 2 233741920 F S_Shore cg09284949 19 51190179 R S_Shore SHANK1(body) cg03701930 10 1981436 F cg03691722 18 7011268 R Island LAMA1(body) cg27466845 11 64397734 F Island NRXN2(body) cg16915863 12 54523294 F S_Shelf LOC400043(body) cg12133451 1 227746453 F Island cg06137123 11 129444480 R cg10501093 14 103593520 R Island TNFAIP2(body) cg08801017 13 19918525 F N_Shore ANKRD26P3(body); LINC00421(tss1500) cg16312514 11 70650521 R SHANK2(body) cg02666610 11 67499431 R cg19321684 6 32159933 R N_Shelf GPSM3(body) cg05076221 7 27182637 F Island HOXA- AS3(body);HOXA5 (body) cg23502204 11 87905295 R N_Shelf RAB38(body) cg20299697 3 138069423 F S_Shore MRAS(body) cg07040013 10 132099553 F cg07509935 14 24780167 F Island CIDEB(body); LTB4R (tss1500); LTB4R2 (body) cg01231141 5 178692691 F ADAMTS2(body) cg22127848 17 64295986 R N_Shelf cg04015962 1 10949192 F cg05226335 11 70253499 R N_Shelf CTTN(body) cg24680439 10 134778467 F N_Shore LOC399829(tss1500) cg00497905 11 76903183 F MYO7A(body) cg23752752 7 4778908 R FOXK1(body) cg11210343 12 95869153 F S_Shore METAP2(body) cg07512361 7 101944430 R Island SH2B2(body) cg05351887 16 3988869 R N_Shore cg01119278 6 110721349 F Island DDO(body) cg19092981 22 19751654 F Island TBX1(body) cg04799958 12 53343849 F S_Shore KRT18(body); KRT8 (tss200) cg19566405 17 33759965 F SLFN12(tss1500) cg26995224 12 121974146 R N_Shore KDM2B(body) cg22841667 11 8705620 F S_Shore RPL27A(body); SNORA3 (tss200); SNORA45 (tss1500) cg07816074 4 8201560 F SH3TC1(body) cg22992730 19 4784940 F N_Shore cg05164926 17 7255624 F Island KCTD11(body) cg01287088 5 176827392 F Island PFN3(body) cg06430632 6 166746926 F SFT2D1(body) cg21697381 17 33759957 R SLFN12(tss1500) cg10885151 13 24270087 F Island cg11057824 14 50471938 F S_Shore C14orf182(body) cg06314111 12 58119915 F Island AGAP2(body); AGAP2- AS1(tss200) cg00551910 14 70037973 R N_Shore CCDC177(body) cg02784823 19 49000897 F Island LMTK3(body) cg04220104 2 179387853 F MIR548N(body); TTN- AS1(body); TTN- AS1(tss200) cg11123440 8 11619852 R C8orf49(body) cg27246571 12 96389588 R Island HAL(body) cg14851700 1 182362230 F S_Shore GLUL(tss1500) cg05396897 1 247611448 R N_Shelf NLRP3(body) cg00873601 12 116044025 R cg04863892 7 27183375 R Island HOXA- AS3(body); HOXA5 (tss200) cg13904806 1 874697 F N_Shore SAMD11(body) cg08610426 19 49249123 F IZUMO1(body) cg01837362 12 34492938 R N_Shore cg18282375 20 3732920 F Island HSPA12B(body) cg14920846 1 201618209 R Island NAV1(body) cg09320662 12 133180698 F S_Shore LRCOL1(body) cg03775991 6 170589530 R Island cg13750264 10 134910540 F N_Shore GPR123(body) cg23188684 11 67383651 F Island DOC2GP(tss1500) cg01331992 9 19379118 R N_Shore RPS6(body) cg19827875 1 201618284 F Island NAV1(body) cg04865726 1 1365911 R S_Shelf cg14898243 10 70863693 R SRGN(body) cg06576532 10 3282437 F cg00011924 17 8301192 R RNF222(tss200) cg03463411 4 81118188 F Island PRDM8(body); PRDM8 (tss1500) cg05836043 18 7011388 F Island LAMA1(body) cg13541527 6 31804078 F S_Shore C6orf48(body); SNORD52 (tss1500) cg03415617 16 34726856 F cg22970003 7 157406032 R Island PTPRN2(body) cg01413354 9 136017755 R N_Shore RALGDS(body) cg17624673 5 140596187 R S_Shore PCDHB13(body) cg10323490 2 88469007 F N_Shore THNSL2(tss1500) cg26056277 2 166982925 F SCN1A(body) cg07637837 18 74824154 F Island MBP(body) cg17187762 22 28070120 R N_Shelf cg09748975 4 4864532 F Island MSX1(body) cg09652312 7 155284062 R Island cg19937979 14 70039915 F Island CCDC177(body) cg22410743 15 67574897 R IQCH(body) cg00344445 11 128647107 R FLI1(body) cg14573099 2 101761014 F TBC1D8(body) cg16322792 1 120165303 F Island ZNF697(body) cg16875104 7 30635889 R S_Shore GARS(body) cg10431713 17 33760230 F SLFN12(tss1500) cg26679004 10 88023135 R Island GRID1(body) cg14371731 10 81003175 R Island ZMIZ1(body) cg10213542 5 178692728 F ADAMTS2(body) cg06473363 6 31631797 F N_Shore GPANK1(body) cg02734505 12 54763081 R Island ZNF385A(body) cg15233961 10 96990543 R cg06470855 13 112997365 R Island cg26135325 1 152595322 R LCE3A(body) cg24049888 11 111250129 F POU2AF1(body) cg20088245 8 1321375 R Island cg03128011 8 1321333 R Island cg24852442 11 76903134 R MYO7A(body) cg19276111 1 24229232 R Island CNR2(body) cg00693004 17 43151433 F NMT1(body) cg16194588 19 49002477 F Island LMTK3(body) cg08551532 19 39998270 F Island DLL3(body) cg16481961 8 1765421 F S_Shore MIR596(body) cg20907614 8 29914963 F cg20806296 2 138582049 F

TABLE 10 Cross-validation results for different effect-size (absolute delta beta, |Δβ|) thresholds at p-value ≦0.05. Shown are the specificity (Spec) and sensitivity (Sens) of the LOO procedure. Specificity is for 1056 normal blood samples derived from GEO (Spec GEO). The total number of significant sites (CGs) in the resulting “Kabuki signature” set, the gene names (Names) and their total number (Genes) corresponding to the significant sites are provided. One optimal combination was selected to be p-value ≦0.05 and |Δβ|>15% (highlighted in bold). The p-values are corrected for multiple testing (Benjamini-Hochberg correction). p-value ≦0.05 Spec |Δβ| Spec Sens (GEO) CGs Names Genes 5% 100.0% 90.9% 99.9% 13595 Not shown 6479 10% 100.0% 90.9% 100.0% 1941 Not shown 1093 15% 100.0% 100.0% 100.0% 287 ADAMTS2; ADO; AGAP2; AGAP2-AS1; ANKRD20A11P; ANKRD26P3; ARHGAP35; 162 ASB2; B4GALT5; BCL11B; BPI; BRCA1; C14orf182; C2CD2L; C6orf48; C8orf49; CASP8; CCDC177; CD37; CDT1; CHCHD7; CHKA; CIDEB; CNR2; CSK; CTTN; CYP2A7; DAGLB; DDO; DLG4; DLL3; DOC2GP; DPY19L1; EBF4; FAM134B; FAM65B; FARSA; FLI1; FLJ31813; FOXA3; FOXK1; GARS; GLUL; GPANK1; GPR123; GPSM3; GRID1; GSTT1; HAL; HLA-DQA1; HOXA-AS3; HOXA4; HOXA5; HOXB7; HOXC4; HSPA12B; IGFBP5; IL17C; IQCH; IZUMO1; KCNK7; KCNQ2; KCTD11; KDM2B; KRT18; KRT8; LAIR2; LAMA1; LAMB2; LAMC3; LCE3A; LINC00421; LMTK3; LOC146880; LOC399829; LOC400043; LOC728392; LRCOL1; LTB4R; LTB4R2; MAP3K7CL; MBP; MCCC1; METAP2; MFI2; MIR548N; MIR596; MRAS; MSX1; MYO1F; MYO7A; NAV1; NBR2; NDRG1; NINJ2; NLRP3; NMT1; NPR2; NRXN2; OR5W2; P4HB; PCDHB13; PFN3; PLAG1; PLD4; PNPLA8; POU2AF1; PRDM2; PRDM8; PTPRN2; RAB38; RAI1; RALGDS; RBMS1; RGL3; RNF216; RNF222; RPL23A; RPL27A; RPS6; RPS8; RRP12; SAMD11; SCN1A; SEMA6B; SFT2D1; SH2B2; SH3PXD2A; SH3RF3; SH3RF3-AS1; SH3TC1; SHANK1; SHANK2; SLC1A4; SLC6A20; SLFN12; SNORA3; SNORA45; SNORD38A; SNORD4A; SNORD52; SNRPC; SPEG; SRGN; SYMPK; SYTL1; TACC2; TBC1D8; TBX1; TCEA2; TEX26; TEX26-AS1; THNSL2; TMEM151B; TNFAIP2; TOX; TTN-AS1; ZMIZ1; ZMYND15; ZNF385A; ZNF697; ZNF890P 20% 100.0% 100.0% 100.0% 46 ADO; AGAP2; AGAP2-AS1; BCL11B; C8orf49; CDT1; DOC2GP; EBF4; 27 FAM65B; GARS; HOXA- AS3; HOXA4; HOXA5; HOXC4; KCNK7; LAMA1; LAMB2; LOC728392; MFI2; MYO1F; NAV1; NINJ2; NLRP3; RNF216; SH3RF3; SH3RF3-AS1; ZMIZ1 25% 100.0% 54.5% 99.1% 10 C8or149; EBF4; FAM65B; HOXC4; KCNK7; MYO1F 6

TABLE 11 Cross-validation results for different effect-size (absolute delta beta, |Δβ|) thresholds at p-value ≦0.01. Shown are the specificity (Spec) and sensitivity (Sens) of the LOO procedure. Specificity is for 1056 normal blood samples derived from GEO (Spec GEO). The total number of significant sites (CGs) in the resulting “Kabuki signature” set, the gene names (Names) and their total number (Genes) corresponding to the significant sites are provided. The p-values are corrected for multiple testing (Benjamini-Hochberg correction). p-value ≦0.01 Spec |Δβ| Spec Sens (GEO) CGs Names Genes 5% 100.0% 90.9% 99.9% 9993 Not shown 5247 10% 100.0% 90.9% 100.0% 1704 Not shown 970 15% 100.0% 100.0% 100.0% 272 ADAMTS2; ADO; AGAP2; AGAP2- 153 AS1; ANKRD26P3; ARHGAP35; ASB2; B4GALT5; BCL11B; BPI; BRCA1; C14orf182; C6orf48; C8orf49; CASP8; CCDC177; CD37; CDT1; CHCHD7; CHKA; CIDEB; CNR2; CSK; CTTN; DAGLB; DDO; DLG4; DLL3; DOC2GP; DPY19L1; EBF4; FAM134B; FAM65B; FARSA; FLI1; FLJ31813; FOXA3; FOXK1; GARS; GLUL; GPANK1; GPSM3; GRID1; HAL; HLA-DQA1; HOXA-AS3; HOXA4; HOXA5; HOXC4; HSPA12B; IGFBP5; IL17C; IQCH; IZUMO1; KCNK7; KCTD11; KDM2B; KRT18; KRT8; LAIR2; LAMA1; LAMB2; LAMC3; LCE3A; LINC00421; LMTK3; LOC146880; LOC399829; LOC400043; LOC728392; LRCOL1; LTB4R; LTB4R2; MAP3K7CL; MBP; MCCC1; METAP2; MFI2; MIR548N; MIR596; MRAS; MSX1; MYO1F; MYO7A; NAV1; NBR2; NDRG1; NINJ2; NLRP3; NMT1; NPR2; NRXN2; OR5W2; P4HB; PCDHB13; PFN3; PLAG1; PLD4; PNPLA8; POU2AF1; PRDM2; PRDM8; PTPRN2; RAB38; RAI1; RALGDS; RBMS1; RGL3; RNF216; RNF222; RPL23A; RPL27A; RPS6; RPS8; RRP12; SAMD11; SEMA6B; SFT2D1; SH2B2; SH3PXD2A; SH3RF3; SH3RF3- AS1; SH3TC1; SHANK1; SHANK2; SLC1A4; SLC6A20; SLFN12; SNORA3; SNORA45; SNORD38A; SNORD4A; SNORD52; SNRPC; SPEG; SRGN; SYMPK; SYTL1; TACC2; TBC1D8; TBX1; TCEA2; TEX26; TEX26-AS1; THNSL2; TMEM151B; TNFAIP2; TOX; TTN- AS1; ZMIZ1; ZMYND15; ZNF385A; ZNF890P 20% 100.0% 100.0% 100.0% 46 ADO; AGAP2; AGAP2- 27 AS1; BCL11B; C8orf49; CDT1; DOC2GP; EBF4; FAM65B; GARS; HOXA- AS3; HOXA4; HOXA5; HOXC4; KCNK7; LAMA1; LAMB2; LOC728392; MFI2; MYO1F; NAV1; NINJ2; NLRP3; RNF216; SH3RF3; SH3RF3-AS1; ZMIZ1 25% 100.0% 81.8% 99.1% 10 C8orf49; EBF4; FAM65B; HOXC4; KCNK7; MYO1F 6

TABLE 12 Cross-validation results for different effect-size (absolute delta beta, |Δβ|) thresholds at p-value ≦0.005. Shown are the specificity (Spec) and sensitivity (Sens) of the LOO procedure. Specificity is for 1056 normal blood samples derived from GEO (Spec GEO). The total number of significant sites (CGs) in the resulting “Kabuki signature” set, the gene names (Names) and their total number (Genes) corresponding to the significant sites are provided. The p-values are corrected for multiple testing (Benjamini-Hochberg correction). p-value ≦0.005 Spec |Δβ| Spec Sens (GEO) CGs Names Genes 5% 100.0% 90.9% 100.0% 8490 Not shown 4680 10% 100.0% 90.9% 100.0% 1569 Not shown 902 15% 100.0% 100.0% 100.0% 267 ADAMTS2; ADO; AGAP2; AGAP2-AS1; ANKRD26P3; 150 ARHGAP35; ASB2; B4GALT5; BCL11B; BPI; BRCA1; C14orf182; C6orf48; C8orf49; CASP8; CCDC177; CD37; CDT1; CHCHD7; CHKA; CIDEB; CNR2; CSK; CTTN; DAGLB; DDO; DLG4; DLL3; DOC2GP; DPY19L1; EBF4; FAM134B; FAM65B; FARSA; FLI1; FLJ31813; FOXA3; FOXK1; GARS; GLUL; GPANK1; GPSM3; GRID1; HAL; HOXA- AS3; HOXA4; HOXA5; HOXC4; HSPA12B; IGFBP5; IL17C; IQCH; IZUMO1; KCNK7; KCTD11; KDM2B; KRT18; KRT8; LAIR2; LAMA1; LAMB2; LAMC3; LCE3A; LINC00421; LMTK3; LOC146880; LOC399829; LOC400043; LOC728392; LRCOL1; LTB4R; LTB4R2; MAP3K7CL; MBP; MCCC1; METAP2; MFI2; MIR548N; MIR596; MRAS; MSX1; MYO1F; MYO7A; NAV1; NBR2; NDRG1; NINJ2; NLRP3; NMT1; NPR2; NRXN2; OR5W2; P4HB; PCDHB13; PFN3; PLAG1; PLD4; PNPLA8; POU2AF1; PRDM2; PTPRN2; RAB38; RAI1; RALGDS; RGL3; RNF216; RNF222; RPL23A; RPL27A; RPS6; RPS8; RRP12; SAMD11; SEMA6B; SFT2D1; SH2B2; SH3PXD2A; SH3RF3; SH3RF3- AS1; SH3TC1; SHANK1; SHANK2; SLC1A4; SLC6A20; SLFN12; SNORA3; SNORA45; SNORD38A; SNORD4A; SNORD52; SNRPC; SPEG; SRGN; SYMPK; SYTL1; TACC2; TBC1D8; TBX1; TCEA2; TEX26; TEX26-AS1; THNSL2; TMEM151B; TNFAIP2; TOX; TTN-AS1; ZMIZ1; ZMYND15; ZNF385A; ZNF890P 20% 100.0% 100.0% 100.0% 46 ADO; AGAP2; AGAP2-AS1; BCL11B; C8orf49; CDT1; DOC2GP; EBF4; 27 FAM65B; GARS; HOXA- AS3; HOXA4; HOXA5; HOXC4; KCNK7; LAMA1; LAMB2; LOC728392; MFI2; MYO1F; NAV1; NINJ2; NLRP3; RNF216; SH3RF3; SH3RF3-AS1; ZMIZ1 25% 100.0% 90.9% 99.1% 10 C8orf49; EBF4; FAM65B; HOXC4; KCNK7; MYO1F 6

TABLE 13 Cross-validation results for different effect-size (absolute delta beta, |Δβ|) thresholds at p-value ≦0.001. Shown are the specificity (Spec) and sensitivity (Sens) of the LOO procedure. Specificity is for 1056 normal blood samples derived from GEO (Spec GEO). The total number of significant sites (CGs) in the resulting “Kabuki signature” set, the gene names (Names) and their total number (Genes) corresponding to the significant sites are provided. The p-values are corrected for multiple testing (Benjamini-Hochberg correction). p-value ≦0.001 Spec |Δβ| Spec Sens (GEO) CGs Names Genes 5% 100.0% 90.9% 100.0% 5492 Not shown 3337 10% 100.0% 100.0% 100.0% 1248 Not shown 745 15% 100.0% 100.0% 100.0% 232 ADAMTS2; ADO; AGAP2; AGAP2- 130 AS1; ANKRD26P3; ARHGAP35; ASB2; B4GALT5; BCL11B; BPI; C14orf182; C6orf48; CASP8; CCDC177; CD37; CDT1; CHCHD7; CHKA; CIDEB; CNR2; CSK; DAGLB; DDO; DLG4; DOC2GP; DPY19L1; EBF4; FAM134B; FAM65B; FARSA; FLI1; FLJ31813; FOXA3; FOXK1; GARS; GLUL; GPANK1; GPSM3; GRID1; HAL; HOXA- AS3; HOXA4; HOXA5; HOXC4; HSPA12B; IGFBP5; IL17C; IQCH; KCNK7; KCTD11; KDM2B; KRT18; KRT8; LAIR2; LAMA1; LAMB2; LAMC3; LCE3A; LINC00421; LMTK3; LOC146880; LOC400043; LOC728392; LRCOL1; LTB4R; LTB4R2; MAP3K7CL; MBP; MCCC1; METAP2; MFI2; MIR548N; MRAS; MSX1; MYO1F; MYO7A; NAV1; NLRP3; NPR2; NRXN2; OR5W2; P4HB; PCDHB13; PFN3; PLAG1; PLD4; PNPLA8; POU2AF1; PRDM2; RGL3; RNF216; RNF222; RPL23A; RPL27A; RPS6; RPS8; RRP12; SAMD11; SEMA6B; SFT2D1; SH2B2; SH3PXD2A; SH3RF3; SH3RF3- AS1; SH3TC1; SHANK1; SLC1A4; SLC6A20; SNORA3; SNORA45; SNORD38A; SNORD4A; SNORD52; SNRPC; SPEG; SRGN; SYMPK; SYTL1; TACC2; TBC1D8; TCEA2; THNSL2; TMEM151B; TNFAIP2; TOX; TTN-AS1; ZMIZ1; ZMYND15; ZNF385A; ZNF890P 20% 100.0% 100.0% 100.0% 43 ADO; AGAP2; AGAP2- 24 AS1; BCL11B; CDT1; EBF4; FAM65B; GARS; HOXA- AS3; HOXA4; HOXA5; HOXC4; KCNK7; LAMA1; LAMB2; LOC728392; MFI2; MYO1F; NAV1; NLRP3; RNF216; SH3RF3; SH3RF3-AS1; ZMIZ1 25% 100.0% 90.9% 98.9% 9 EBF4; FAM65B; HOXC4; KCNK7; MYO1F 5

TABLE 14 Cross-validation results for different effect-size (absolute delta beta, |Δβ|) thresholds at p-value ≦0.0001. Shown are the specificity (Spec) and sensitivity (Sens) of the LOO procedure. Specificity is for 1056 normal blood samples derived from GEO (Spec GEO). The total number of significant sites (CGs) in the resulting “Kabuki signature” set, the gene names (Names) and their total number (Genes) corresponding to the significant sites are provided. The p-values are corrected for multiple testing (Benjamini-Hochberg correction). p-value ≦0.0001 Spec |Δβ| Spec Sens (GEO) CGs Names Genes 5% 100.0% 100.0% 100.0% 2696 Not shown 1822 10% 100.0% 100.0% 100.0% 801 Not shown 504 15% 100.0% 100.0% 100.0% 181 ADO; AGAP2; AGAP2- 104 AS1; ARHGAP35; ASB2; BCL11B; BPI; C14orf182; C6orf48; CASP8; CCDC177; CD37; CDT1; CHCHD7; CHKA; CIDEB; CNR2; CSK; DAGLB; EBF4; FAM134B; FAM65B; FLJ31813; FOXK1; GARS; GPANK1; GPSM3; GRID1; HAL; HOXA- AS3; HOXA4; HOXA5; HOXC4; HSPA12B; IGFBP5; IL17C; IQCH; KCNK7; KCTD11; KDM2B; KRT18; KRT8; LAIR2; LAMA1; LAMB2; LCE3A; LMTK3; LOC146880; LOC400043; LRCOL1; LTB4R; LTB4R2; METAP2; MFI2; MIR548N; MSX1; MYO1F; MYO7A; NAV1; NLRP3; NRXN2; P4HB; PCDHB13; PFN3; PLAG1; PLD4; PNPLA8; PRDM2; RGL3; RNF216; RPL23A; RPL27A; RPS6; RPS8; RRP12; SAMD11; SEMA6B; SH2B2; SH3PXD2A; SH3RF3; SH3RF3- AS1; SH3TC1; SHANK1; SLC1A4; SLC6A20; SNORA3; SNORA45; SNORD38A; SNORD4A; SNORD52; SPEG; SRGN; SYTL1; TACC2; TBC1D8; TCEA2; THNSL2; TMEM151B; TNFAIP2; TOX; TTN-AS1; ZMIZ1; ZMYND15; ZNF385A 20% 100.0% 100.0% 100.0% 39 ADO; AGAP2; AGAP2-AS1; BCL11B; CDT1; EBF4; FAM65B; GARS; HOXA- 23 AS3; HOXA4; HOXA5; HOXC4; KCNK7; LAMA1; LAMB2; MFI2; MYO1F; NAV1; NLRP3; RNF216; SH3RF3; SH3RF3-AS1; ZMIZ1 25% 100.0% 81.8% 98.9% 9 EBF4; FAM65B; HOXC4; KCNK7; MYO1F 5

TABLE 15 Cross-validation results for different effect-size (absolute delta beta, |Δβ|) thresholds at p-value ≦0.00001. Shown are the specificity (Spec) and sensitivity (Sens) of the LOO procedure. Specificity is for 1056 normal blood samples derived from GEO (Spec GEO). The total number of significant sites (CGs) in the resulting “Kabuki signature” set, the gene names (Names) and their total number (Genes) corresponding to the significant sites are provided. The p-values are corrected for multiple testing (Benjamini-Hochberg correction). p-value ≦0.00001 Spec |Δβ| Spec Sens (GEO) CGs Names Genes 5% 100.0% 100.0%   100.0%   1188 Not shown 893 10% 100.0% 100% 100% 447 ADO; AFAP1; AFAP1-AS1; AGAP2; AGAP2- 314 AS1; AKT3; ANKRD30B; ANXA6; ARHGAP31; ARHGAP32; ARHGEF7; ARL5C; ARPC1B; ARSG; ASB2; ASUN; ATP11A; ATP6V1G2- DDX39B; AXIN1; BAG2; BAHCC1; BCL11B; BRD2; C10orf11; C12orf79; C1orf53; C6orf48; C6orf62; C9orf106; CACNA1H; CACNG8; CAMTA1; CAPZB; CASP8; CASZ1; CBLN2; CCDC88A; CCT7; CD37; CDT1; CIAPIN1; CNR2; CNST; CNTN5; COMT; COQ2; COQ9; COX4I2; CRIP2; CSK; CSNK2B; CSRP2BP; CXXC1; DAGLB; DBX2; DDX39A; DDX39B; DGKI; DIO2; DIO2- AS1; DLG4; DOK1; DZIP3; EBF4; EEF1D; EFNA1; EMILIN2; ERH; ETS1; EVA1B; EVC2; EXOC8; FAM110D; FAM63A; FAM65B; FGF20; FIGNL2; FLCN; FLJ12825; FLJ31813; FNDC3B; FOXK1; FOXN3; FST; FYB; GARS; GAS5; GMDS- AS1; GNG4; GNG7; GOLGA3; GPANK1; GPSM3; GUK1; HAL; HBP1; HDAC4; HIC1; HLA- DOA; HLX; HNRNPA1; HNRNPA1P10; HNRNPH1; HOXA- AS3; HOXA4; HOXA5; HOXA6; HOXC4; HSPA12B; IBTK; IGFBP5; IL17C; IL17RE; INPP5E; IRAK3; JMJD1C; KAT6A; KCNA2; KCNAB1; KCNK7; KDM2B; KDM3A; KIAA1524; KIRREL3; KLHDC7B; KRT18; KRT8; KTI12; LAMA1; LAMB2; LAMP1; LHX6; LMTK3; LOC146880; LOC389705 LONP2; LOXL3; LPAR5; LPAR6; LRBA; LSM3; LSR; LTB4R; LTBR; LYAR; MAB21L2; MAP3K6; MARS2; MBNL1; MBOAT2; MDH1; MEN1; METAP2; MFI2; MICAL3; MIR1296; MIR4285; MIR4520B; MIR4763; MIR548AE2; MIR548N; MIRLET7A3; MIRLET7B; MIRLET7BHG; MRPL44; MRPS15; MRPS18B; MSX1; MXRA8; MYL1; MYO1F; NAV1; NDUFA3; NFXL1; NPM1; NRXN2; NTMT1; NUFIP2; OR5B17; ORAI2; OSCAR; P4HB; PAPPA; PARK7; PARP4; PCF11; PCGF3; PDE4A; PDE6A; PET117; PEX13; PFN3; PHGDH; PKP1; PLD4; PLIN5; PM20D2; PNO1; POLE2; PPP1R10; PRDM2; PSMD14; PTDSS2; PTPN6; PURG; PUS10; RAB11FIP3; RASAL1; RASGRP2; RB1; RBFOX3; RMDN2; RMDN2- AS1; RNF216; RNF38; RPAP3; RPL14; RPL23A; RPL35; RPL37; RPS12; RPS18; RPS6; RPS8; RPSA; RRP12; RUFY1; SAMD11; SCARF2; SCNN1A; SEC31B; SEMA6B; SERTAD4; SH2B2; SH3PXD2A; SH3RF3; SH3RF3- AS1; SH3TC1; SIX2; SLC16A6; SLC17A5; SLC1A4; SLC33A1; SLC39A9; SLC50A1; SLC6A20; SLCO3A1; SLMAP; SMIM8; SMYD2; SNORA33; SNORA6; SNORD100; SNORD38A; SNORD44; SNORD4A; SNORD52; SNORD72; SNORD75; SNORD76; SNORD77; SNORD78; SNORD79; SNORD80; SNX27; SNX6; SPRTN; SRGN; SRSF1; SSTR5-AS1; STAU2- AS1; TACC2; TAF8; TAOK3; TAP2; TBC1D22A; TBC1D8; TBX4; TCERG1L; TCFL5; TCHH; TFAP2E; TFB2M; TGFBI; THNSL2; TNFAIP2; TNR; TOX; TRAK1; TRIM67; TTN-AS1; TVP23A; TXNDC12; UBA6; UBA6- AS1; UBE2J2; UBE2R2; UPF1; USP42; VOPP1; VPS52; WDR37; WRN; YAP1; ZBTB49; ZC3H12D; ZFAND2A; ZKSCAN1; ZMIZ1; ZMYND15; ZNF385A; ZNF787; ZSWIM8; ZSWIM8-AS1 15% 100.0% 100% 100% 111 ADO; AGAP2; AGAP2- 67 AS1; ASB2; BCL11B; C6orf48; CASP8; CD37; CDT1; CNR2; CSK; DAGLB; EBF4; FAM65B; FLJ31813; FOXK1; GARS; GPANK1; GPSM3; HAL; HOXA- AS3; HOXA4; HOXA5; HOXC4; HSPA12B; IGFBP5; KDM2B; KRT18; KRT8; LAMA1; LAMB2; LOC146880; LTB4R; METAP2; MFI2; MIR548N; MYO1F; NAV1; NRXN2; PFN3; PLD4; PRDM2; RNF216; RPL23A; RPS6; RPS8; RRP12; SAMD11; SEMA6B; SH3PXD2A; SH3RF3; SH3RF3- AS1; SH3TC1; SLC1A4; SLC6A20; SNORD38A; SNORD4A; SNORD52; SRGN; TACC2; TBC1D8; THNSL2; TNFAIP2; TOX; TTN- AS1; ZMIZ1; ZMYND15 20% 100.0% 100% 100% 29 ADO; AGAP2; AGAP2- 18 AS1; BCL11B; CDT1; EBF4; FAM65B; GARS; HOXA4; HOXC4; LAMA1; LAMB2; MFI2; MYO1F; RNF216; SH3RF3; SH3RF3-AS1; ZMIZ1 25% 100.0% 90.9%  99.7%  6 FAM65B; HOXC4; MYO1F 3

TABLE 16 Three additional CpG loci corresponding to two genes were identified as showing a statistically significant (corrected p value <0.01) difference in CS and non-CS controls. “Mean not-CHARGE refers to the mean β-value for the CpG loci in the non-CS cases “Mean CHARGE” refers to the mean β-value for the CpG loci in the CS samples. Benjamini- Hochberg DNA corrected p- Absolute methylation Mean not- Mean Gene Illumina ID p-value value deltaBeta deltaBeta effect CHARGE CHARGE Symbol Genome_Build cg14422498 1.33E−06 0.00308636 −0.10816215 0.10816215 LOSS 0.3808205 0.272658 37 cg18657389 3.17E−06 0.00541815 −0.10816215 0.11427340 LOSS 0.7313574 0.617084 EVPL 37 cg25285743 7.66E−07 0.00228633 0.10211681 0.10211681 GAIN 0.5901659 0.692283 LMO3 37 Genomic Coordinate Relation to (NCBI, transcription Illumina ID Strand Chromosome hg19) Relation_to_UCSC_CpG_Island start site (TSS) cg14422498 R 9 100639423 cg18657389 R 17 74023630 EVPL (tss200) cg25285743 F 12 16701533 LMO3 (3′UTR); LMO3 (3′UTR)

TABLE 17 75 additional CpG loci corresponding to 28 Genes were identified as showing a statistically significant (p-value ≦0.05) difference in KS and non-KS controls. “Mean not-Kabuki” refers to the mean beta-value for the CpG loci in the non-KS cases. “Mean Kabuki” refers to the mean beta-value for the CpG loci in the KS samples. Benjamini- Hochberg DNA corrected p- absDelta Methylation Mean not- Illumina ID p-value value deltaBeta Beta Effect Kabuki Mean Kabuki Gene Symbol cg03657281 0.000545826 0.039123657 −0.22287617 0.222876166 Loss 0.707974691 0.485098525 SLC22A23; SLC22A23; SLC22A23; SLC22A23 cg24169822 2.96E−06 0.003253178 −0.20777829 0.207778288 Loss 0.447022355 0.239244067 HOXA4 cg03724423 1.78E−05 0.007879037 −0.18951298 0.189512977 Loss 0.381331218 0.191818242 HOXA4 cg04321618 2.96E−06 0.003253178 −0.1859374 0.1859374 Loss 0.4195601 0.2336227 HOXA4 cg14700524 9.91E−05 0.017451795 −0.17890157 0.178901568 Loss 0.815736418 0.63683485 cg12876594 4.44E−05 0.012044763 −0.17715184 0.177151839 Loss 0.497182273 0.320030433 NPR2 cg04317399 9.91E−05 0.017451795 −0.17562929 0.175629286 Loss 0.390439536 0.21481025 HOXA4; HOXA4 cg24869272 6.66E−05 0.014525204 −0.17496219 0.174962191 Loss 0.409302991 0.2343408 TSPAN4; TSPAN4; TSPAN4; TSPAN4; TSPAN4; TSPAN4; TSPAN4 cg05783384 1.04E−05 0.006001284 −0.17302223 0.17302223 Loss 0.789634755 0.616612525 cg06942814 2.81E−05 0.009696319 −0.17288902 0.17288902 Loss 0.515048645 0.342159625 HOXA4 cg07967717 1.48E−06 0.002458452 −0.17177699 0.171776986 Loss 0.368327636 0.19655065 CNR2 cg17457637 0.000205609 0.024756111 −0.16984355 0.169843545 Loss 0.386699145 0.2168556 HOXA4 cg25952581 1.48E−06 0.002458452 −0.16970611 0.169706108 Loss 0.4250646 0.255358492 HOXA4 cg23510089 2.81E−05 0.009696319 −0.16888611 0.168886111 Loss 0.767464827 0.598578717 cg07548255 9.91E−05 0.017451795 −0.16867061 0.168670608 Loss 0.585514391 0.416843783 SH3RF3; SH3RF3- AS1; SH3RF3-AS1 cg13935577 4.44E−05 0.012044763 −0.16856998 0.168569985 Loss 0.783746818 0.615176833 BTBD11; BTBD11 cg24201793 1.48E−06 0.002458452 −0.16739174 0.167391739 Loss 0.648610864 0.481219125 MBOAT2 cg25702651 6.66E−05 0.014525204 −0.16688422 0.166884217 Loss 0.4287815 0.261897283 cg02483029 0.000545826 0.039123657 −0.16379654 0.163796542 Loss 0.701013409 0.537216867 SPIDR; SPIDR; SPIDR; SPIDR cg23884241 2.96E−06 0.003253178 −0.16298323 0.162983233 Loss 0.5839747 0.420991467 HOXA4 cg11685316 0.000736644 0.045047655 −0.16281272 0.162812723 Loss 0.737778573 0.57496585 MFSD6L; MFSD6L cg19497523 9.91E−05 0.017451795 −0.16142362 0.16142362 Loss 0.850669945 0.689246325 TMPRSS9 cg08883485 0.000205609 0.024756111 −0.16112403 0.16112403 Loss 0.484720964 0.323596933 NAV1 cg15122841 5.92E−06 0.004562719 −0.15940461 0.159404613 Loss 0.807004455 0.647599842 HDAC4 cg21801165 1.48E−06 0.002458452 −0.15885777 0.158857765 Loss 0.530243582 0.371385817 cg15630950 9.91E−05 0.017451795 −0.15795437 0.157954371 Loss 0.604374155 0.446419783 HLA-DOA cg03534375 2.96E−06 0.003253178 −0.15335462 0.153354621 Loss 0.535936055 0.382581433 SLMAP cg00921309 1.78E−05 0.007879037 −0.15220007 0.152200067 Loss 0.5088694 0.356669333 cg02713669 6.66E−05 0.014525204 −0.15216219 0.152162194 Loss 0.292733764 0.14057157 SH3RF3; SH3RF3- AS1; SH3RF3-AS1 cg26040809 0.000545826 0.039123657 −0.1514797 0.151479697 Loss 0.829734064 0.678254367 ADARB2 cg07021906 5.92E−06 0.004562719 −0.15122097 0.15122097 Loss 0.736244036 0.585023067 SLC7A5 cg02142461 1.48E−06 0.002458452 −0.15102594 0.151025942 Loss 0.568091 0.417065058 LYAR; LYAR; ZBTB49 cg00562553 2.96E−06 0.003253178 −0.15088227 0.15088227 Loss 0.801209545 0.650327275 HOXA4 cg26125366 1.48E−06 0.002458452 −0.1508059 0.150805895 Loss 0.361926945 0.21112105 cg14270725 5.92E−06 0.004562719 −0.15076771 0.150767705 Loss 0.317108145 0.16634044 MXRA8; MXRA8; MXRA8; MXRA8; MXRA8 cg23926439 0.000545826 0.039123657 0.150255174 0.150255174 Gain 0.535782309 0.68603748 cg02661079 2.81E−05 0.009696319 0.151650952 0.151650952 Gain 0.364989373 0.516640325 CDH22 cg10193721 9.91E−05 0.017451795 0.152593035 0.152593035 Gain 0.190991682 0.343584717 LTB4R; LTB4R2; CIDEB; CIDEB; LTB4R2 cg12301347 4.44E−05 0.012044763 0.152873337 0.152873337 Gain 0.288168655 0.441041992 cg08352439 1.48E−06 0.002458452 0.153206312 0.153206312 Gain 0.582253655 0.735459967 VOPP1 cg24363820 0.000205609 0.024756111 0.153494638 0.153494638 Gain 0.476936145 0.630430783 CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CHKB-CPT1B cg27619353 1.04E−05 0.006001284 0.154038076 0.154038076 Gain 0.388803791 0.542841867 LGALS1 cg25334934 5.92E−06 0.004562719 0.15465902 0.15465902 Gain 0.651384455 0.806043475 cg10290504 0.000205609 0.024756111 0.154996448 0.154996448 Gain 0.199256269 0.354252717 cg10770023 0.000205609 0.024756111 0.155278024 0.155278024 Gain 0.452634409 0.607912433 CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CHKB- CPT1B cg26631039 0.000143483 0.020691318 0.155448474 0.155448474 Gain 0.137210776 0.29265925 GLI2 cg05654765 0.000143483 0.020691318 0.157465431 0.157465431 Gain 0.389924327 0.547389758 LAMB2; LAMB2 cg08498747 1.48E−06 0.002458452 0.158076749 0.158076749 Gain 0.569288509 0.727365258 cg16276982 0.000736644 0.045047655 0.158794724 0.158794724 Gain 0.306828109 0.465622833 cg26986681 0.000400864 0.033829502 0.160018319 0.160018319 Gain 0.231060573 0.391078892 IGFBP7-AS1 cg16081457 2.96E−06 0.003253178 0.1612716 0.1612716 Gain 0.5209443 0.6822159 cg05156901 0.000736644 0.045047655 0.161506804 0.161506804 Gain 0.555427955 0.716934758 CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CHKB- CPT1B cg22344745 0.000143483 0.020691318 0.162023911 0.162023911 Gain 0.537765064 0.699788975 cg20517050 1.78E−05 0.007879037 0.162136438 0.162136438 Gain 0.650460645 0.812597083 HOXA5; HOXA- AS3 cg01308968 0.000545826 0.039123657 0.164175747 0.164175747 Gain 0.575034036 0.739209783 IGFBP7-AS1 cg12765123 0.000400864 0.033829502 0.165860469 0.165860469 Gain 0.574899173 0.740759642 cg03294458 0.000545826 0.039123657 0.166611545 0.166611545 Gain 0.12960339 0.296214935 WNK4 cg10932486 1.04E−05 0.006001284 0.167217033 0.167217033 Gain 0.556037109 0.723254142 cg02916332 1.78E−05 0.007879037 0.168286167 0.168286167 Gain 0.608908 0.777194167 HOXA5; HOXA- AS3 cg26310551 0.000288445 0.028858761 0.168287927 0.168287927 Gain 0.252093282 0.420381208 LTB4R; LTB4R2; CIDEB; CIDEB; LTB4R2 cg17432857 1.04E−05 0.006001284 0.168403377 0.168403377 Gain 0.606293673 0.77469705 HOXA5; HOXA- AS3 cg07330481 1.48E−06 0.002458452 0.169538806 0.169538806 Gain 0.573050227 0.742589033 ARL5C; ARL5C cg23129930 6.66E−05 0.014525204 0.170164367 0.170164367 Gain 0.569918991 0.740083358 HOXA6; HOXA- AS3; HOXA-AS3 cg02248486 5.92E−06 0.004562719 0.174414246 0.174414246 Gain 0.672434445 0.846848692 HOXA5; HOXA5; HOXA- AS3 cg18737081 2.96E−06 0.003253178 0.175031395 0.175031395 Gain 0.734303555 0.90933495 ZMIZ1 cg11178337 2.96E−06 0.003253178 0.175563736 0.175563736 Gain 0.129339864 0.3049036 cg11724970 1.04E−05 0.006001284 0.176344059 0.176344059 Gain 0.683343091 0.85968715 HOXA5; HOXA- AS3 cg19112186 0.000205609 0.024756111 0.178717061 0.178717061 Gain 0.529355164 0.708072225 CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CPT1B; CHKB- CPT1B cg27053299 1.48E−06 0.002458452 0.18052838 0.18052838 Gain 0.557516736 0.738045117 CLYBL cg02721176 0.000400864 0.033829502 0.186840873 0.186840873 Gain 0.256788927 0.4436298 CCDC172 cg02005600 5.92E−06 0.004562719 0.190667314 0.190667314 Gain 0.654958836 0.84562615 HOXA5; HOXA- AS3 cg26516362 0.000400864 0.033829502 0.193294684 0.193294684 Gain 0.271304049 0.464598733 RUFY1; RUFY1; RUFY1 cg19759481 1.04E−05 0.006001284 0.216017614 0.216017614 Gain 0.605454836 0.82147245 HOXA5; HOXA5; HOXA- AS3 cg20354552 0.000736644 0.045047655 0.220332162 0.220332162 Gain 0.08997291 0.310305072 SLFN12 cg20744163 2.96E−06 0.003253178 0.23788097 0.23788097 Gain 0.723305764 0.961186733 ZMIZ1 Genomic Relation to Coordinate transcription start Illumina ID Genome_Build Chromosome (NCBI, hg19) Strand Relation_to_UCSC_CpG_Island site (TSS) cg03657281 37 6 3270030 F SLC22A23 (body) cg24169822 37 7 27170994 F S_Shore HOXA4 (tss1500) cg03724423 37 7 27170755 R S_Shore HOXA4 (tss1500) cg04321618 37 7 27170880 R S_Shore HOXA4 (tss1500) cg14700524 37 10 3282231 R cg12876594 37 9 35791798 F Island NPR2 (tss1500) cg04317399 37 7 27170313 F Island HOXA4 (body) cg24869272 37 11 850296 R Island TSPAN4 (body) cg05783384 37 2 218843735 R Island cg06942814 37 7 27170819 F S_Shore HOXA4 (tss1500) cg07967717 37 1 24229682 F S_Shore CNR2 (body) cg17457637 37 7 27170717 F S_Shore HOXA4 (tss1500) cg25952581 37 7 27170961 R S_Shore HOXA4 (tss1500) cg23510089 37 4 73531188 F cg07548255 37 2 109746754 F Island SH3RF3 (body); SH3RF 3-AS1 (tss200) cg13935577 37 12 107974897 R Island BTBD11 (body) cg24201793 37 2 9144764 F S_Shore MBOAT2 (tss1500) cg25702651 37 3 192675515 R cg02483029 37 8 48297271 R SPIDR (body) cg23884241 37 7 27169957 R Island HOXA4 (body) cg11685316 37 17 8702564 R Island MFSD6L (body) cg19497523 37 19 2425476 R Island TMPRSS9 (body) cg08883485 37 1 201619787 F Island NAV1 (body) cg15122841 37 2 240181892 F HDAC4 (body) cg21801165 37 13 50210231 F cg15630950 37 6 32976897 R S_Shore HLA-DOA (body) cg03534375 37 3 57743163 R S_Shore SLMAP (tss200) cg00921309 37 8 48091487 F cg02713669 37 2 109746691 R Island SH3RF3 (body); SH3RF3- AS1 (tss200) cg26040809 37 10 1505626 F N_Shore ADARB2 (body) cg07021906 37 16 87866833 R SLC7A5 (body) cg02142461 37 4 4293079 R S_Shore LYAR (tss1500); ZBTB49 (body) cg00562553 37 7 27169740 F Island HOXA4 (body) cg26125366 37 18 31806577 R S_Shore cg14270725 37 1 1289806 R Island MXRA8 (body) cg23926439 37 1 228890884 R Island cg02661079 37 20 44829722 R Island CDH22 (body) cg10193721 37 14 24780691 F Island CIDEB (tss200); LTB4R (tss200); LTB4R2 (body) cg12301347 37 22 46285638 R Island cg08352439 37 7 55637123 F N_Shelf VOPP1 (body) cg24363820 37 22 51016703 R Island CHKB- CPT1B (body); CPT1B (body); CPT1B (tss200) cg27619353 37 22 38071651 F N_Shore LGALS1 (body) cg25334934 37 2 121269348 R cg10290504 37 11 116578271 F Island cg10770023 37 22 51016644 R Island CHKB- CPT1B (body); CPT1B (body); CPT1B (tss200) cg26631039 37 2 121625022 F Island GLI2 (body) cg05654765 37 3 49170727 F LAMB2 (tss200) cg08498747 37 17 41747669 R cg16276982 37 15 29968032 R S_Shore cg26986681 37 4 58060609 R N_Shore IGFBP7-AS1 (body) cg16081457 37 12 81103680 R S_Shore cg05156901 37 22 51016646 R Island CHKB- CPT1B (body); CPT1B (body); CPT1B (tss200) cg22344745 37 1 227746294 F Island cg20517050 37 7 27183806 R Island HOXA- AS3 (body); HOXA5 (tss 1500) cg01308968 37 4 58061859 R Island IGFBP7-AS1 (body) cg12765123 37 10 132100019 F cg03294458 37 17 40935998 R Island WNK4 (body) cg10932486 37 5 61028265 F cg02916332 37 7 27183591 F Island HOXA- AS3 (body); HOXA5 (tss 1500) cg26310551 37 14 24780540 F Island CIDEB (body); LTB4R (tss 200); LTB4R2 (body) cg17432857 37 7 27184438 R Island HOXA- AS3 (body); HOXA5 (tss 1500) cg07330481 37 17 37322330 F S_Shore ARL5C (body) cg23129930 37 7 27186993 F Island HOXA- AS3 (body); HOXA6 (body) cg02248486 37 7 27183196 R Island HOXA- AS3 (body); HOXA5 (body) cg18737081 37 10 80999807 F N_Shelf ZMIZ1 (body) cg11178337 37 17 43065745 R cg11724970 37 7 27182493 R N_Shore HOXA- AS3 (body); HOXA5 (body) cg19112186 37 22 51016638 R Island CHKB- CPT1B (body); CPT1B (body); CPT1B (tss200) cg27053299 37 13 100548780 F Island CLYBL (body) cg02721176 37 10 118084587 R CCDC172 (body) cg02005600 37 7 27183686 R Island HOXA- AS3 (body); HOXA5 (tss 1500) cg26516362 37 5 178986906 F Island RUFY1 (body) cg19759481 37 7 27183401 R Island HOXA- AS3 (body); HOXA5 (tss 200) cg20354552 37 17 33760249 F SLEN12 (tss1500) cg20744163 37 10 80999841 F N_Shelf ZMIZ1 (body)

REFERENCES

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1. A method of detecting and/or screening for CHARGE syndrome (CS), or an increased likelihood of CS, in a human subject, comprising: determining a sample methylation profile from a sample comprising DNA from said subject, said sample profile comprising the methylation level of at least 3, optionally at least 5, at least 8, at least 10, at least 25, at least 44, at least 50, at least 75, at least 100, at least 125, at least 140, or all CpG loci from (i) Tables 2 and/or 16 and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i); and (a) determining the level of similarity of said sample profile to one or more control profiles, wherein (i) a high level of similarity of the sample profile to a CS specific control profile; (ii) a low level of similarity to a non-CS control profile: and/or (iii) a higher level of similarity to a CS specific control profile than to a non-CS control profile indicates the presence of, or an increased likelihood of, CS; and/or (b) determining a sample methylation profile from a sample comprising DNA from said subject, said sample profile comprising the methylation level of at least 2, optionally at least 3, at least 4, at least 6, at least 8, at least 10, at least 16, at least 20, at least 25, at least 30, at least 35, at least 40, or all the genes from Tables 2 and/or 16: and determining the level of similarity of said sample profile to one or more control profiles, wherein (i) a high level of similarity of the sample profile to an CS specific control profile; (ii) a low level of similarity to a non-CS control profile: and/or (iii) a higher level of similarity to a CS specific control profile than to a non-CS control profile indicates the presence of or an increased likelihood of, CS.
 2. The method of claim 1, wherein the selected CpG lad comprise CpG loci from Tables 2 and/or 16 having an absolute CS delta-beta value≧0.10, ≧0.11, ≧0.12, ≧0.13, ≧0.15, ≧0.18, ≧0.20 or ≧0.22; and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (I). 3-12. (canceled)
 13. The method of claim 1, wherein a high level of similarity to the control profile is indicated by a correlation coefficient between the sample profile and the control profile having an absolute value between 0.5 to 1, optionally between 0.75 to 1, and a low level of similarity to the control profile is indicated by a correlation coefficient between the sample profile and the control profile having an absolute value between 0 to 0.5, optionally between 0 to 0.25; and/or wherein a higher level of similarity to the CS specific profile than to the non-CS control profile is indicated by a higher correlation value computed between the sample profile and the CS specific profile than an equivalent correlation value computed between the sample profile and the non-CS control profile, optionally wherein the correlation value is a correlation coefficient.
 14. (canceled)
 15. (canceled)
 16. The method of claim 1, wherein methylation level is measured as a β-value. 17-22. (canceled)
 23. The method of claim 1, wherein the sample is derived from blood, fibroblast tissue, buccal tissue, lymphoblastoid cell line, saliva or a prenatal sample, optionally a CVS, placenta, circulating fetal DNA and/or amniotic fluid sample.
 24. The method of claim 1, wherein he human subject is a fetus, 25-30. (canceled)
 31. A method of determining a course of management for an individual with CHARGE syndrome (CS), or an increased likelihood of CS, comprising: a) identifying an individual with CS or an increased likelihood of CS, according to the method of claim 1; and b) assigning a course of management for CS and/or symptoms of a CS, comprising) testing for at least one medical condition associated with CS and ii) applying an appropriate medical intervention based on the results of the testing.
 32. The method of claim 31, wherein the medical condition is selected from ophthalmic colobomas, cardiovascular anomalies, hearing loss, airway conditions such as choanal atresia/stenosis or tracheoesophageal fistula, feeding issues, retinal detachment, growth delay, delayed puberty, renal anomalies, developmental difficulties, behavioural problems, dual sensory loss and neuropsychological issues such as attention deficit hyperactivity disorder or autism.
 33. A kit for detecting and/or screening for CHARGE syndrome, or an increased likelihood of CS, in a sample, comprising: a) at least one detection agent for determining the methylation level of: i) at least 3, optionally at least 5, at least 8, at least 10, at least 25, at least 44, at least 50, at least 75, at least
 100. at least 125, at least 140, or all CpG loci from (i) Tables 2 and/or 16 and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (1); and/or ii) at least 2, optionally at least 3, at least 4, at least 6, at least 8, at least 10, at least 16, at least 20, at least 25, at least 30, at least 35, at least 40, or all the genes from Tables 2 and/or 16; and b) instructions for use. 34, (Currently amended) The kit according to claim 33, further comprising bisulfite conversion reagents, methylation-dependent restriction enzymes, methylation-sensitive restriction enzymes, PCR reagents, probes, primers and/or a computer-readable medium that causes a computer to compare methylation levels from a sample at the selected CpG loci to one or more control profiles and compute a correlation value between the sample and control profile.
 35. (canceled)
 36. A method of detecting and/or screening for Kabuki syndrome (KS), or an increased likelihood of KS, in a human subject, comprising: determining a sample methylation profile from a sample comprising DNA from said subject, said sample profile comprising the methylation level of at least 6, optionally at least 8, at least 10, at least 15, at least 20, at least 25, at least 46, at least 50, at least 75, at least 100, at least 125, at least 150, at least 200, at least 250, or all CpG loci from (i) Tables 9 and/or 17 and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i); and (a) determining, the level of similarity of said sample profile to one or more control profiles, wherein (i) a high level of similarity of the sample profile to a KS specific control profile; (ii) a low level of similarity to a non-KS control profile; and/or (iii) a higher level of similarity to a KS specific control profile than to a non-KS control profile indicates the presence of, or an increased likelihood of, KS: and/or (b) determining a sample methylation profile from a sample comprising DNA from said subject, said sample profile comprising the methylation level of at least 3, optionally at least 4, at least
 6. at least 8 at least 10 at least 15, at least 20 at least 25, at least 50, at least 75, at least 100, at least 125, or all the genes from Tables 9 and/or 17; and determining the level of similarity of said sample profile to one or more control profiles, wherein (i) a high level of similarity of the sample profile to an KS specific control similarity to a KS specific control profile than to non-KS control profile indicates the presence of, or an increased likelihood of, KS.
 37. The method of claim 36, wherein the selected CpG loci comprise CpG loci from Tables 2 and/or 16 having an absolute KS delta-beta value ≧0.15, optionally ≧0.16, ≧0.18, ≧0.20, ≧0.22, ≧0.24 or ≧0.25; and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i). 38-45. (canceled)
 46. The method of claim 36, wherein a high level of similarity to the control profile is indicated by a correlation coefficient between the sample profile and the control profile having an absolute value between 0.5 to 1, optionally between 0.75 to 1, and a low level of similarity to the control profile is indicated by a correlation coefficient between the sample profile and the control profile having an absolute value between 0 to 0.5, optionally between 0 to 0.25; and/or wherein a higher level similarity to the KS specific profile than to the non-KS control profile is indicated by a higher correlation value computed between the sample profile and the KS specific profile than an equivalent correlation value computed between the sample profile and the non-KS control profile, optionally wherein the correlation value is a correlation coefficient.
 47. (canceled)
 48. (canceled)
 49. The method of claim 36, wherein methylation level is measured as a β-value. 50-55. (canceled)
 56. The method of claim 36, wherein the sample is derived from blood, fibroblast tissue, buccal tissue, lymphoblastoid cell line, saliva or a prenatal sample, optionally a CVS, placenta, circulating fetal DNA and/or amniotic fluid sample,
 57. The method of claim 36, wherein the human subject is a fetus. 58-63. (canceled)
 64. A method of determining a course of management for an individual with Kabuki syndrome (KS), or an increased likelihood of KS, comprising: a) identifying an individual with KS or an increased likelihood of KS according to the method of claim 36; and b) assigning a course of management for KS and/or symptoms of a KS, comprising i) testing for at least one medical condition associated with KS and ii) applying an appropriate medical intervention based on the results of the testing.
 65. The method of claim 64 wherein the medical condition is selected from ophthalmic abnormalities, cardiovascular anomalies, hearing loss, kidney, abnormalities, skeletal anomalies, dental abnormalities, feeding difficulties, endocrine problems, infection, autoimmune disorders, seizures and developmental disorders.
 66. A kit for detecting and/or screening for Kabuki syndrome, or an increased likelihood of KS, in a sample, comprising: a) at least one detection agent for determining the methylation level of; iii) at least 6, optionally at least 8, at least 10, at least 15, at least 20, at least 25, at least 46, at least 50, at least 75, at least 100, at least 125, at least 150, at least 200, at least 250, or all CpG loci from (i) Tables 9 and/or 17 and/or (ii) associated CpG loci residing within 300 nucleotides, optionally within 150 nucleotides, of the CpG loci of (i); and/or iv) at least 3, optionally at least 4, at least 6, at least 8, at least 10, at least 15, at least 20, at least 25, at least 50, at least 75, at least 100, at least 125, or all the genes from Tables 9 and 17; and b) instructions for use.
 67. The kit according to claim 66, further comprising bisulfite conversion reagents, methylation-dependent restriction enzymes, methylation-sensitive restriction enzymes, PCR reagents, probes, primers and/or a computer-readable medium that causes a computer to compare methylation levels from a sample at the selected CpG loci to one or more control profiles and compute a correlation value between the sample and control profile.
 68. (canceled) 